UNIVERSITY OF CINCINNATI

Date:__February 4, 2005__ I, _____________________Daniel Alford_______________________,

hereby submit this work as part of the requirements for the degree of:

Master of Science

in:

Mechanical Engineering

It is entitled:

Lightweight, Low Cost, Automotive

Data Acquisition and Telemetry System

This work and its defense approved by:

Chair: _Dr. Randall Allemang________ _Dr. David Brown_____________ _Dr. Allyn Phillips__________

Lightweight, Low Cost, Automotive

Data Acquisition and Telemetry System

A Thesis Submitted to:

Division of Research and Advanced Studies Of The University of Cincinnati

In Partial Fulfillment of the Requirements for the Degree of:

MASTER OF SCIENCE

In the Department of Mechanical, Industrial, and Nuclear Engineering Of the College of Engineering

2005

By:

Daniel Alford

B.S., University of Cincinnati, 2002

Committee Chair: Dr. Randall Allemang Committee: Dr. David Brown

Dr. Allyn Phillips

Abstract

Dynamically, testing small, lightweight automobiles can be very difficult on a limited

budget. Several quality data acquisition systems exist that are designed for automotive

applications, but most are large cumbersome units that require an AC power inverter. It

would not be possible to use one of these systems on a small lightweight vehicle, like a

Formula SAE racecar. A device powered from a 12 volt DC power supply is required for

this type of application. Also, typical data acquisition systems are far too bulky and

costly to be fitted to a Formula SAE vehicle. As with any system, recording actual

operating conditions will provide useful information, leading to system improvements.

In order to achieve these goals, several data acquisition with telemetry systems were

investigated. The most interesting system, Dataq DI-720-EN and Linksys wireless

router, were purchased and characterized through a barrage of testing. The system was

first characterized by logging a random signal and transferring the data through a typical

wired setup. The system was then methodically tested in the lab with Ethernet wired and

wireless data transfer methods. The data acquisition and telemetry system was also tested

dynamically, on the 2004 Bearcat Motorsports FSAE vehicle, using wireless data transfer

methods. The testing results were promising, with minimal signs of poor data. Some

gaps were seen in the data, but were deemed insignificant for the majority of potential

testing. Lastly, several likely improvements are discussed.

Acknowledgements

I would like to thank everyone that has been involved in the development of this thesis.

To my committee members Dr. Randall Allemang, Dr. David Brown, and Dr. Allyn

Phillips, I thank you all for your support, because without it this research would not have

been completed.

I want to especially thank my advisor, Dr. Randall Allemang, for his countless hours of

discussion and guidance at nearly any hour of the day. The teaching assistant position for

Automotive Design I, II, and III not only paid the bills, but has also enhanced my

engineering and leadership skills just as much if not more than all of my years in the

undergraduate mechanical engineering program at the University of Cincinnati. I am

amazed by your ability to find support for student projects and research interests.

I want to thank PCB Piezotronics for their donation of six single axis capacitive

accelerometers. Thanks to Todd Dahling and Brian Lewis of Manta Corporation for

loaning the SoMat and Freewave equipment. To all of the students and staff of the

SDRL, I want to wish the best of luck, you made the days more enjoyable and there was

never a dull moment. Thanks to all my friends and family for all the motivation to

complete this research. I should also thank the Bearcat Motorsports teams of 2003, 2004,

and 2005 for the use of their race car for testing.

Thank you all.

Oh, and thanks B.J. Stoney and Bill Wise for manufacturing those parts.

Table of Contents Table of Contents................................................................................................................. i List of Figures .................................................................................................................... iii List of Tables ...................................................................................................................... v List of Tables ...................................................................................................................... v Chapter 1 Introduction ........................................................................................................ 1

1.1 - Introduction ............................................................................................................ 1 Chapter 2 Background ........................................................................................................ 6

2.1 - Telemetry Systems ................................................................................................. 6 2.2 - Data Acquisition Systems....................................................................................... 7

2.2.1 - Larson Davis DSS ........................................................................................... 8 2.2.2 - National Instruments Compact RIO .............................................................. 10 2.2.3 - Motec ADL.................................................................................................... 13 2.2.4 - Pi Research Delta System.............................................................................. 14 2.2.5 - DaTaq DI-720-EN ......................................................................................... 15

2.3 - Data Acquisition Theory ...................................................................................... 26 2.3.1 - Sampling Theory ........................................................................................... 26 2.3.2 - Aliasing.......................................................................................................... 27

Chapter 3 Applications ..................................................................................................... 29 3.1 - Lab Testing ........................................................................................................... 29

3.1.1 - Parallel Port: .................................................................................................. 30 3.1.2 - Local Area Network: ..................................................................................... 34 3.1.3 - Wireless Local Area Network: ...................................................................... 41 3.1.4 - Wireless LAN w/Parabolic Antenna ............................................................. 44

3.2 - Onsite Testing....................................................................................................... 46 3.2.1 - Static Testing ................................................................................................. 47 3.2.2 - Dynamic Testing............................................................................................ 56

Chapter 4 Conclusions & Future Work ............................................................................ 59 References......................................................................................................................... 63 Appendix - A Instrumentation ..................................................................................... 66 Appendix - B Matlab Script......................................................................................... 71

B.1 CSV Converter.................................................................................................. 71 B.2 Main Executable ............................................................................................... 73 B.3 Windows ........................................................................................................... 79 B.4 Load Data.......................................................................................................... 81 B.5 Cyclic Averaging .............................................................................................. 83 B.6 Overlapping....................................................................................................... 86 B.7 Autopower......................................................................................................... 88 B.8 Crosspower ....................................................................................................... 89 B.9 H1...................................................................................................................... 92 B.10 Hv...................................................................................................................... 93 B.11 H2...................................................................................................................... 94 B.12 Multiple Coherence for H1 ............................................................................... 95 B.13 Multiple Coherence for Hv ............................................................................... 95

i

B.14 Plot Output ........................................................................................................ 96 B.15 Plot Output 2 ..................................................................................................... 99

ii

List of Figures Figure 1.1 Basic System ..................................................................................................... 4 Figure 2.1 Channel Selection............................................................................................ 16 Figure 2.2 Max Sample Rate Selection............................................................................. 17 Figure 2.3 Sample Rate Selection..................................................................................... 17 Figure 2.4 Channel Settings Selection .............................................................................. 19 Figure 2.5 Sine Wave on +-10V Scale ............................................................................. 20 Figure 2.6 Sine Wave on +-5V Scale ............................................................................... 21 Figure 2.7 Fixed Calibration ............................................................................................. 22 Figure 2.8 Low Calibration............................................................................................... 22 Figure 2.9 High Calibration .............................................................................................. 23 Figure 2.10 Sine Wave Phase Shift................................................................................... 24 Figure 2.11 Parallel Sampling Example ........................................................................... 25 Figure 2.12 Multiplexed Sampling (Equal Sampling & Scan Rates) ............................... 25 Figure 2.13 Multiplexed Sampling (Non-equal Sampling & Scan Rates)........................ 26 Figure 2.14 Sampling Relationships ................................................................................. 27 Figure 2.15 Aliasing Example .......................................................................................... 27 Figure 2.16 Aliasing Contribution .................................................................................... 28 Figure 3.1 Parallel Port Testing Block Diagram............................................................... 30 Figure 3.2 Parallel Port Frequency Response Functions .................................................. 33 Figure 3.3 Typical Coherence........................................................................................... 33 Figure 3.4 Parallel Port Random Time History ................................................................ 34 Figure 3.5 Wired Ethernet Block Diagram....................................................................... 34 Figure 3.6 LAN Frequency Response Functions.............................................................. 36 Figure 3.7 LAN Sine Wave Time History ........................................................................ 38 Figure 3.8 LAN Sine Wave Close Up .............................................................................. 39 Figure 3.9 Bellcrank Rotation........................................................................................... 40 Figure 3.10 Basic Wireless System Block Diagram......................................................... 41 Figure 3.11 WLAN Frequency Response Functions ........................................................ 42 Figure 3.12 WLAN Sine Wave Time History .................................................................. 43 Figure 3.13 Parabolic Testing Block Diagram ................................................................. 44 Figure 3.14 Parabolic Frequency Response Functions ..................................................... 45 Figure 3.15 Parabolic Sine Wave Time History ............................................................... 46 Figure 3.16 Vehicle Testing Block Diagram .................................................................... 46 Figure 3.17 Center Hill FRFs............................................................................................ 48 Figure 3.18 Center Hill Coherence ................................................................................... 49 Figure 3.19 Bad Channels FRFs ....................................................................................... 50 Figure 3.20 Bad Channels Coherence............................................................................... 51 Figure 3.21 Bad Channels................................................................................................. 52 Figure 3.22 Throttle Position (Engine Off)....................................................................... 53 Figure 3.23 Throttle Position (Engine On) ....................................................................... 53 Figure 3.24 Typical Acceleration ..................................................................................... 54 Figure 3.25 Isolated Accelerometer Acceleration ............................................................ 55 Figure 3.26 Isolated Accelerometer.................................................................................. 55 Figure 3.27 Bad Channel FRF .......................................................................................... 57 Figure 3.28 Bad Channel Time History............................................................................ 57

iii

Figure 3.29 Dynamic Frequency Response Functions ..................................................... 58 Figure A.1 Wiring Diagram.............................................................................................. 67 Figure A.2 Damper Position ............................................................................................. 69 Figure A.3 Lateral Accelerometer .................................................................................... 70 Figure A.4 Accelerometers ............................................................................................... 71

iv

List of Tables Table 2.1 Performance Information of DSS DSIT ............................................................. 9 Table 2.2 Performance Information for RIO I/O Modules ............................................... 12 Table 2.3 Delta Systems ................................................................................................... 14 Table 2.4 Data Acquisition Device Comparison .............................................................. 15 Table 2.5 Channel Comparison......................................................................................... 15 Table 2.6 IOS Example Parameters .................................................................................. 19 Table 2.7 Resolution Increases with applied Gain............................................................ 21 Table 3.1 Vehicle Sensors................................................................................................. 29 Table 3.2 Lab Test Setups................................................................................................. 30 Table 3.3 Data Transfer Throughput ................................................................................ 31 Table 3.4 Random & Sine Logging Parameters ............................................................... 35 Table 3.5 Vehicle Logging Parameters............................................................................. 35 Table 3.6 MTS Test File Information ............................................................................... 39 Table 3.7 Onsite Test Setups ............................................................................................ 47 Table 3.8 Center Hill Logging Parameters ....................................................................... 49 Table A.1 Suggested Sampling Frequency....................................................................... 68

v

Chapter 1 Introduction

1.1 - Introduction

Over the history of motor racing, racers have been looking for the elusive perfect vehicle. In

reality a perfect vehicle does not exist, at least not for a reasonably difficult course with varying

weather conditions. To be competitive, a team needs to know what their vehicle will be doing on

track. In the early days of racing, the only feedback race engineers had, was what they could see

and what the driver could see, feel, and hear. This seat of the pants data logging was really the

only solution to perfecting a race car at the time, computers were unheard of. Once computers

were invented, they were still too heavy and bulky to use on a race car, so the seat of the pants

method was still utilized.

The major concerns when selecting a data acquisition device are size, cost, expandability, and

capability. Some data acquisition devices are now small, lightweight, and almost affordable, so

data logging has become the preferred solution to finding the perfect vehicle, because a data

logger does not lie, or miss anything, like a driver might. While a data logger is a great

improvement from the seat of the pants method, it can still be improved. Data acquisition is not

all benefit, it has drawbacks too. Some of the drawbacks are price, weight, and memory. Weight

is less of an issue when it comes to a racing series that has a minimum weight rule, but in

Formula SAE there is no such rule, so every pound counts. Cost of a data acquisition system can

be very overwhelming. Some systems cost well above $100,000, sensors included, well beyond

the budget of most racers. Another downfall of a data logger is that the data has to be

downloaded after the race and then processed. This means no change can be made during a pit

stop to improve the vehicles performance and win the race; but it can produce an abundance of

1

information that the team could use to prepare a better vehicle in the future. The typical racecar

data acquisition system contains an analog to digital converter for each channel of data to be

collected and some form of memory. The memory, where all of the digitized signals are stored

for later analysis, can be very pricey. To reduce the cost of memory, the amount of data taken

could be reduced, by recording fewer channels or lowering the sampling frequency. Either could

lead to many headaches for the race engineer; there may be something important happening that

was not observed, which would lead to a large amount of wasted time searching for a problem

that cannot be seen.

Telemetry is the transfer of data from the data acquisition system to another data analysis system

in real time and wirelessly. If the race engineer could use telemetry to analyze the vehicle data

as it comes in, the engineer could deduce improvements for the vehicle-driver system. When the

vehicle comes in for a pit stop the team can make modifications based on the data the engineer

analyzed from the last track stint. The current method in racing is the use of a dual band UHF

radio frequency to telemeter data. Another popular option is to log the data, and then transfer the

data once a lap. Telemetry is currently too costly for widespread use outside of the top levels of

professional motorsports. This thesis will explore the possibility of using a new technology,

wireless local area networks (Wireless LAN or WLAN) as a means of transferring data at a much

lower cost. Wireless local area networks are not that different from the dual band UHF radios

currently used. Their benefit is the lower cost structure, and the drawback is the reduced range.

Looking back 50 years, it seems quite obvious on where the biggest advantage to be gained was;

and 50 years from now it will be quite obvious where the biggest improvement will be. But as of

right now, racecar engineers are spending enormous amounts of time and money trying to find

2

the smallest advantage. The top teams in Formula 1 (F1) are spending in the range of $700

million a year to find any advantage they can. While the F1 teams may not be too concerned

with a budget, at least not a normal sized budget, club racers, who are into racing only for the

thrill, are concerned with how every penny is spent, because they are on a tight budget of their

own hard earned money. The wireless local area network technology is not currently expected to

work at a range large enough to encompass a typical racetrack, but it could be of use at a vehicle

dynamics testing area, or a parking lot used for testing. Although the range is currently limited,

the wireless local area networking technology is rapidly changing and may produce a

technological advance capable of significantly increasing the range. This technology could also

be very useful to the SCCA Solo2 racers, as their autocrosses typically run in open parking lots.

Where club racers may spend $10,000-20,000 a year, autocrossers are more concerned with their

budget; they will probably only spend around $2,000 a year. Figure 1.1 depicts the basic

concept, utilized to approach this lightweight, low cost, automotive data acquisition and

telemetry system. A data acquisition and telemetry system of this type would be in the $2,000

range where a professional motorsports data acquisition and telemetry system is 5-10 times that.

3

Wireless Game Adapter Sensors

Data Acquisition Device

Wireless Router

Wireless Link

Figure 1.1 Basic System

A Linksys wireless router, Linksys game adapter, Cisco access point with parabolic antenna, and

Dataq DI-720-EN were chosen to be characterized by the testing for this document, for their low

cost, small size and ability to be powered by a 12 volt DC power supply. The IEEE has created

standards utilized by several manufacturers to create wireless networking devices. The 802.11b

at 11 Mbps and 802.11g at 54 Mbps standards operate on a 2.4 GHz frequency band, and differ

only by their throughput capability. Another standard, 802.11a operates in a 5 GHz frequency

band and has capacity similar to the 802.11g standard, but had a decreased range due to its

higher operating frequency. At the time of procurement, there were no game adapters available

operating in the 802.11a and g wireless standards, therefore the 802.11b standard was chosen for

testing. The Cisco access point, also working in the 802.11b standard, and parabolic antenna

were borrowed from the UCIT department, to test for the additional range expected by using a

parabolic antenna. The Dataq unit was the chosen data acquisition device, because of its

Ethernet output, low cost, small size, and ability to be powered by the 12 volt DC power supply

4

available on the test vehicle. The Dataq unit is a multiplexed system, which means that the

multiple data channels are not being digitized simultaneously.

5

Chapter 2 Background

2.1 - Telemetry Systems

Historically, telemetry used an analog frequency modulation, normally operating in the FM radio

broadcast band, to transfer data from one sensor to the data logger. That meant if ten sensors

needed to be logged, ten distinctive FM radio frequencies were required to transmit the data.

These systems have many disadvantages including interference from FM radio stations, and

frequency shift issues. The FM broadcast band is fairly populated, so finding a frequency band

large enough to transfer large amounts of data has become nearly impossible. Frequency drift

issues arise when the transmitter is located in a thermally active area, imposing another

limitation.

There are some more complex ways around the drawbacks of FM telemetry, FM-FM and PWM-

FM. FM-FM modulation performs a frequency modulation of the signal on a low frequency

carrier, and then on the radio carrier frequency. This works well for near DC signals, but when

rapidly changing sensor signals, high bandwidth signals, are being observed, FM-FM telemetry

is much less useful. Pulse width modulation-FM first modulates samples of the signal as the

time duration of short pulses, and then frequency-modulates the pulses.[19] The main

drawback is that when the signal to noise ratio of the recovered signal becomes low, the receiver

cannot determine the precise frequency.

6

2.2 - Data Acquisition Systems

Typically in motorsports and automotive testing several different types of data are recorded.

They may range from various temperatures, pressures, positions, accelerations, and many more.

These data types create an additional problem, knowing how to record them. Many vehicle

signals are nearly DC, therefore very low frequency, while others are very active, changing

several hundred thousand times a second, and some are on or off (digital). The ideal system

would allow the user to set each channels sample rate, gain, and calibration individually. Many

temperatures are slow to change, therefore a low sampling frequency (1-2 Hz) would be desired

to reduce the amount of memory need, and eliminate extraneous information. Brake rotor

temperatures may need to be sampled more frequently than other temperatures, due to their quick

changes during braking, but it is unlikely to reach more than 10 Hz. Probably, the most extreme

case for the need to sample at high frequency would be to record cylinder pressure. For a

motorcycle engine the speeds can reach above 15,000 rpm, and attempting to capture the

pressure change of the explosion of fuel and air requires a sampling frequency in the 100,000 Hz

range. It is highly unlikely cylinder pressures would ever be recorded on a vehicle, as this is

much more common during engine dynamometer tuning. A multiplexed data acquisition system

would not be recommended for measuring multiple cylinder pressures, as in this case it is

important to know the relative time difference between peak cylinder pressures, and a

multiplexed unit will not provide an accurate estimate. Much more reasonable high frequency

signals likely to be measured on a vehicle are the damper positions, and strain gauges placed on

particular parts. Strain gauges and damper positions are typically sampled at 500 Hz. A

sampling frequency of 500 Hz and vehicle moving at 30 mph, results in traveled distance of

about 1 inch between data points. Many motorsports data acquisition devices contain inputs

7

specifically for rpm measurements, which contain signal conditioning that converts the rpm

sensors output to a recordable signal. Digital inputs are also quite common in data acquisition

systems, and are used to record on/off situations. Digital inputs are typically used to signify fan

and button/switch usage. Another requirement for a lightweight, low cost, automotive data

acquisition and telemetry system is the ability to be powered by a 12 volt DC power source.

Several data acquisition systems exist that will fit in the back seat of a typical automobile, and

can be plugged into a power inverter, but these systems are far too large, bulky, and heavy to be

used on a racecar, let alone a Formula SAE racecar.

There are several hundred, maybe thousand, data acquisition systems available commercially.

The difficulty lies in finding a suitable low cost solution that provides room for future expansion.

The comparison discussion for this thesis will be limited to five systems, two popular

motorsports data acquisition systems, two lab systems, and the chosen Dataq DI-720-EN. The

Motec ADL and Pi Research Delta systems are the motorsports specific systems, while the

Larson Davis DSS and National Instruments Compact RIO are oriented more towards the lab

testing environment.

2.2.1 - Larson Davis DSS

The Larson Davis Digital Sensing System (DSS) is typically used to record structural dynamics,

sound pressure, and vehicle/engine noise. The DSS consists of a Master Module with either a

parallel or Ethernet port for communicating with a personal computer; it also stores the digitized

data. The Ethernet port allows this system to be used with the same 802.11 wireless networking

technology explored throughout this thesis. The 8 slots of the DSS can be filled with any

combination of receiver, source, and/or data acquisition modules. A DSS full of data acquisition

8

modules is capable of logging 64 channels. The DSS utilizes a new technology, Digital Sensor

Interface Transmitters (DSIT), to reduce the cost of expensive low noise cabling by locating a 24

bit ADC near the sensor. A 24 bit ADC could provide a significant advantage over lower bit

units, as a higher number of bits increases the available dynamic range and provides less need to

fill the entire dynamic range. The DSIT digitizes the signals and sends the digitzed data to the

receiver card in the mainframe through inexpensive ribbon cabling. The DSITs are capable of

sampling all channels simultaneously at sampling frequencies up to 61 kHz. One, two, or three

sensor DSITs are available with direct voltage inputs. Many sensors require an ICP power

source and some DSITs are capable of providing ICP power. Another useful feature was the

ability to read the Transducer Electronic Data Sheets (TEDS) data on the sensor. In todays

world of high channel data acquisition systems, it becomes cumbersome and tedious to keep a

good record of test setups. TEDS sensors attempt to alleviate this record keeping problem by

storing sensor specific information in the sensor. This information ranges from the standard

information like serial number, model number, and calibration sensitivity to user defined fields.

Table 2.1 describes the performance characteristics of the available DSITs. For

Table 2.1 Performance Information of DSS DSIT

9

2.2.2 - National Instruments Compact RIO

The National Instruments company produces several types of data acquisition devices, from PC

card based applications to the mainframe and card systems. The system of particular interest in

this application was the Compact RIO. One of the most interesting features of the Compact RIO

was its size, only 3.5 by 3.5 by 10.75 inches for the premium system. The Compact RIO is

developed around the RIO (Reconfigurable Input Output) FPGA (Field Programmable Gate

Array) technology. The Compact RIO has a similar architecture as the Larson Davis DSS. They

both contain a chassis integrated, real time processor with bays for input-output modules. The

Compact RIO can operate as a control system as well as operating as a data acquisition device.

The Compact RIO system utilizes National Instruments graphical programming software,

LabVIEW, to control the input-output modules. This means that the user must write a program

using LabVIEW to control the input-output modules. The real time processor, the brains behind

the Compact RIO, controls the I/O modules independently which allows the I/O modules to

operate in parallel. The real time controller also contains the communication ports, serial and

Ethernet, again enabling the Compact RIO to be used with 802.11 wireless networking

technology. The Compact RIO allows the user to define the measurement hardware circuitry

using FPGA chips and LabView. The field programmable gate arrays are the major contributors

to the Compact Rios low cost, reconfigurability, and high performance features. National

Instruments has developed several modules to be used with the Compact RIO. Analog input

modules are available for small (+-80 mV), medium (+-10 V), and large (+-60 V) voltage inputs,

as well as thermocouples and a +-5 volt module with simultaneous sampling, antialias filtering,

10

and TEDS capability. Table 2.2 lists all of the input-output modules available for the Compact

RIO and their features.

11

Table 2.2 Performance Information for RIO I/O Modules

12

2.2.3 - Motec ADL

The Advanced Dash Logger (ADL) from Motec is one of the most popular motorsports data

acquisition devices. It combines a data acquisition device with a dash that can be used to display

vehicle information to the driver. The dash display is configurable, allowing the user to

determine what information is important for the driver. It can be used to replace many if not all

of the gauges found on a typical race car dash. The tachometer has 70 segments that can be set

up to a maximum of 19,000 rpm, and the shift point can also be programmed such that the

redline is dependent upon gear selection. The current gear is also displayed. The dash has 3 user

definable numeric displays and 1 alpha numeric display; which can be used to display any of the

200+ ECU (Engine Control Unit) or ADL channels available.

The standard Motec ADL comes with 6 (12 bit ADC) analog voltage inputs, 4 analog

temperature inputs, 2 speed inputs, 2 digital inputs, 4 switch inputs, and 4 auxiliary outputs for a

total of 22 I/O (input-output) channels. For an additional cost it can be expanded to 50 I/O

channels as follows, 20 analog voltage inputs, 8 analog temperature inputs, 4 speed inputs, 4

digital inputs, 4 switch inputs, 2 low voltage analog inputs, and 8 auxiliary outputs. The analog

inputs have a signal input range of 0-15 volts. The ADL is capable of sampling 8 channels at

1000 Hz, and the rest at 500 Hz, with the exception of the ECU channels which can be sampled

at a maximum of 20 Hz. The standard system comes with 384 kilobytes of memory for digitized

signal storage and can be upgraded to 8 megabytes. All of the systems allow 40 channels from a

Motec ECU to be input through an RS232 connection. Obviously, 50 ADL channels plus 40

ECU channels does not match the 200+ channels previously mentioned. The additional Motec

ADL channels are derived from any combination of the 90 ADL and ECU channels. These math

13

channels provide additional flexibility to the ADL. The base system has a cost of about $4600,

while the 50 I/O system sells for about $6500; Motec also sells a 30 I/O system for about $5100.

The memory expansions also add significant cost to the system, ranging from $900 to $1875 for

1 to 8 Mb respectively. Telemetry, which enables the user to view real time data, is an additional

cost of $5300 for the radios and $1100 to enable this option in the ADL. The total cost of a

system with telemetry could range from $11,000 to almost $15,000.

2.2.4 - Pi Research Delta System

Pi Research is one of the most respected data logging companies in motorsports. They sell a

variety of systems from their entry level System 1 to their top of the line Sigma Elite system.

The System 1 contains 6 analog inputs, wheel speed, rpm, lap time and lateral acceleration. The

System 1 provides a good start into race car data acquisition, but it does not provide enough

flexibility to be appropriately compared with the previously mentioned systems. The Sigma

Elite comes standard with 40 analog channels and 8 digital channels with 128 Mb of memory; it

can be expanded to 64 analog channels and 12 digital channels. This system was eliminated

from discussion due to its complexity and expected cost. It is typically only used in the highest

levels of professional motorsports. The Delta and Delta Lite systems are very similar. They

only differ in expandability, storage memory and telemetry, seen in Table 2.3.

Delta Lite Delta Expandable No Yes Telemetry No Yes

Memory (Mb) 2 8

Table 2.3 Delta Systems

The basic Delta system contains 10 analog inputs, 2 wheel speeds, lap time, rpm, 2 accelerations,

and ECU signals. The system can be expanded to provide an additional 24 analog inputs and 2

wheel speeds. For additional costs, the Delta system can be attached to a dash and/or a telemetry

14

system. The radios for the telemetry option, operate in the 450-470 MHz frequency range, and

require an appropriate license from the FCC. The Delta system is capable of logging 0-5 volt

signals at sampling rates up to 500 Hz. Table 2.4 provides a general system comparison of the

five devices, while Table 2.5 details the quantity of the different type of channels.

Device ADC (bits)

Channel Count

Max Sampling Frequency (Hz)

Size (in X in X in)

Type Price

DSS 24 64 61,000 6 X 9.25 X 14.5 Simultaneous Unknown Compact RIO 12-24 64 800,000 3.5 X 3.5 X 10.75 Both ~$6,000

Motec ADL 12 22-50 1,000 1.5 X 3.5 X 7 Simultaneous $4,600-$6,500

Pi Delta 10 42 500 1.5 X 4 X 4 Unknown Unknown Dataq 16 44 250,000 1.5 X 7 X 9 Multiplexed $1,500

Table 2.4 Data Acquisition Device Comparison

Device Analog Input

Temperature Input

Digital Input

ECU Input

Speed Input

Auxilary Outputs

DSS 0-64 0 0 0 0 0-32 Compact

RIO 0-64 0-32 0-64 0 0-64 0-64

Motec ADL 6-20 4-8 4-8 40 2-4 4-8 Pi Delta 10-34 0 0 Unknown 2-4 0 Dataq 32 0 8 0 0 4

Table 2.5 Channel Comparison

2.2.5 - DaTaq DI-720-EN

The data acquisition device chosen was the DI-720-EN, from Dataq. The Dataq device was

chosen for its low price tag, number of channels, and because it can use the Ethernet to transfer

data. The cost of the device and the software was about $2500. The DI-720-EN can transfer

data from the real time controller to the logging computers hard disc by a parallel cable or, it can

be set up as a network device and the data can be transferred over a local area network. This

Ethernet data transfer method made it particularly interesting, because of the possibilities of

coupling the Dataq device with existing 802.11 wireless networking technologies. The DI-720-

EN only records voltage signals, so some sensors will not work directly with the DI-720-EN.

15

Two such examples are thermocouples and strain gauges, these sensors will require signal

conditioning. The DI-720-EN contains 32 single-ended or 16 differential channels as well as 8

digital inputs and 4 digital outputs. A differential channel is a channel that has a positive signal,

and a negative signal; these signals are then referenced against each other. A differential channel

should only need to be used when a potential exists between the ground for the data acquisition

device and the ground for the sensor. Figure 2.1 shows the channel selection window.1

Figure 2.1 Channel Selection

WINDAQ/Pro was the version of Dataqs software used throughout the testing for this paper,

which allows for a maximum sample rate of 250 kHz. The Dataq system utilizes multiplexed

data acquisition which means that the maximum sampling frequency must be divided

1 To setup channels follow the path and directions at the bottom of the window: Edit Channels

16

(multiplexed across) each channel. The maximum sample rate is the rate at which the analog to

digital converter (ADC) digitizes the signal. The rate at which each channel is being digitized is

the maximum sample rate divided by the number of channels enabled. The maximum sample

rate is often referred to as the scan rate in a multiplexed system, Figure 2.22.

Figure 2.2 Max Sample Rate Selection

The maximum sample rate should not be confused with the sample rate; an example will be

presented after the discussion of Dataqs intelligent over sampling (IOS) feature. The sample

rate is the frequency that the software displays and records the digitized signal. It should be

noted that this sample rate is the total sample rate or throughput rate. For example, if sixteen

channels were enabled and the sample rate was set to 16,000 samples per second then each

channel would contain 1000 data points per second recorded or the analog signal of each channel

would be digitized every 1 thousandth of a second. Figure 2.3 shows the sample rate window3.

Figure 2.3 Sample Rate Selection

2 Set the maximum sample rate by following this path: Edit Preferences Max Sample Rate or Ctl+F3 3 Setup can be performed by following this path: Edit Sample Rate or F3

17

The DI-720-EN is equipped with a feature, Dataq calls intelligent over sampling. Intelligent

over sampling is a feature of the DI-720-EN that allows the data that was digitized between

sample points to be used. When the sample rate and the maximum sample rate are not set equal,

the analog to digital converter will digitize the signal at the maximum sample rate divided by the

number of enabled channels; then the processor will down sample digitized data to the user

defined sample rate divided by the number of enabled channels. There are several processes that

can be used while down sampling: average, last point, maximum, minimum, root mean square,

and frequency. The average acquisition method will sum the value of all of the points between

the sample rate points and scale it by the number of points; while the last point method will only

retain the last point. Logically, the maximum and minimum methods will retain the maximum or

minimum value. The RMS acquisition method will take a root mean square average of the

points. Figure 2.4 shows the window used to select the acquisition method and gain level.4

4 To setup the acquisition method and/or gain follow this path: Edit Channel Settings

18

Figure 2.4 Channel Settings Selection

As mentioned earlier, an example will be discussed to further clarify the intelligent over

sampling topic, the parameters of which are shown below in Table 2.6.

Maximum Sample Rate 50000 Channels 2

Sample Rate 100

IOS Method Last Point

Table 2.6 IOS Example Parameters

A signal sent into each of the two channels will be digitized by the ADC at a rate of 25,000 times

per second. The software will then be set to sample at 100 Hz, which means the hardware

created 500 times more data points than required. With the last point method this means every

500th point will be recorded to the logging computers hard disk.

19

The DI-720-EN allows each channel to have a gain applied to it. Applying a gain to a sensor

signal is useful to maximize the amount of the dynamic range being used. For example, a sine

wave characterized by the equation below, which would look like Figure 2.5, when recorded on a

+-10 volt scale.

2)sin( += xy (2.1)

0 1 2 3 4 5 6 7 8 9 10-10

-8

-6

-4

-2

0

2

4

6

8

10

Time

Am

plitu

de

Sine Wave on +-10V Scale

Figure 2.5 Sine Wave on +-10V Scale

But, if the same signal was gained so that the dynamic range was +-5 volts, it would appear as

Figure 2.6 shows. A quick comparison of the two figures shows that the amplitude of the sine

wave was much more defined in Figure 2.6, therefore more of the dynamic range is being used

and the resolution of the signal is better.

20

0 1 2 3 4 5 6 7 8 9 10-5

-4

-3

-2

-1

0

1

2

3

4

5

Time

Am

plitu

de

Sine Wave on +-5V Scale

Figure 2.6 Sine Wave on +-5V Scale

Gain Scale (V)

Resolution (mV/bit)

1 10 1.22

2 5 0.61

4 2.5 0.31

8 1.25 0.15

Table 2.7 Resolution Increases with applied Gain

Table 2.7, shows the maximum resolution of a signal on each scale. There are two ways

available to calibrate the channels. The first method, called Fixed Calibration, requests the

engineering value equivalent to a +10 volt signal and a -10 volt signal from the sensor, as shown

in Figure 2.7.5

5 The fixed calibrator can be found by following this path: Edit Fixed Calibration or F12

21

Figure 2.7 Fixed Calibration

The second method uses a low and high calibrator value (sometimes called a two-point method).

Using the low and high calibrator method requires the lowest and highest voltage level as well as

the appropriate engineering value for that voltage, setup seen in Figure 2.8 and Figure 2.9.6 7

Figure 2.8 Low Calibration

6 The low calibrator can be found following this path: Edit Low Calibration F9

7 The high calibrator can be found following this path: Edit High Calibration or F11

22

Figure 2.9 High Calibration

The DI-720-EN is a multiplexed data acquisition device, meaning each channel is not digitized at

the same instance as the other channels. This leads to a phase shift, which is most easily seen by

logging the same sine wave on multiple channels. Once the channels are overlaid, as in Figure

2.10, it is quite obvious that the sine wave is shifted (delayed in time). It should be noted that

channel one is the curve furthest to the right and the channel number increases moving to the left.

It is important to note, because this implies channel one was the last to be logged, but this was

not the case. It is a function of how the data was plotted; a single time array was used for all of

the channels. At first glance this seems backwards, that channel two leads channel one, but upon

a closer look at the details it makes perfect sense. If a signal is logged at time t0 on channel one

and again at t0+t on channel two, and so on, and the channels are then plotted against the same

time array it will appear as if channel two leads channel one, as in Figure 2.10. While this shift

is not detrimental, it must be known so that the data can be corrected and plotted appropriately.

23

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time

Vol

t

Time History

Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10Ch 11Ch 12Ch 13Ch 14Ch 15Ch 16

0.018 0.019 0.02 0.021 0.022 0.023

0.52

0.53

0.54

0.55

0.56

0.57

0.58

0.59

0.6

Time

Vol

t

Time History

Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10Ch 11Ch 12Ch 13Ch 14Ch 15Ch 16

Figure 2.10 Sine Wave Phase Shift

Many high priced data acquisition systems sample all channels simultaneously, while the lower

priced systems contain fewer ADCs and must sample each channel separately. The ideal data

acquisition system utilizes a parallel sample and hold technique that samples all channels at one

instant, seen in Figure 2.11. Figure 2.12 depicts a multiplexed system digitizing a sine wave on

four channels with the scan rate and sample rate equal to one another, notice the time delay

between data points from channel to channel. This delay is the scan rate and in this case it was

also the sample rate. Figure 2.13 shows the sampling characteristics of a multiplexed system

when the scan rate and sample rate are not equal. In this figure, the scan rate is the time delay

from channel to channel and the sample rate is related to the time elapsed between the data

points of one channel.

24

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Sig

nal

Parallel Sampling

0 1 2 3 4 5 6 7 8 9 10-1

0

1C

h 1

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

2

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

3

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Time (s)

Ch

4

Figure 2.11 Parallel Sampling Example

0 1 2 3 4 5 6 7 8 9 10-1

0

1Multiplexed Sampling (Sampling = Scan)

Sig

nal

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

1

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

2

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

3

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

4

Figure 2.12 Multiplexed Sampling (Equal Sampling & Scan Rates)

25

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Sig

nal

Multiplexed Sampling (not equal)

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

1

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

2

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Ch

3

0 1 2 3 4 5 6 7 8 9 10-1

0

1

Time (s)

Ch

4

Figure 2.13 Multiplexed Sampling (Non-equal Sampling & Scan Rates)

2.3 - Data Acquisition Theory

2.3.1 - Sampling Theory

Sampling theory is defined by Shannons Sampling Theorem, which states:

21 =

= Nyqsamp FtF (2.2)

maxFFNyq (2.3)

These equations describe the maxim frequency content that can be described by data logged at

any chosen sampling frequency. Figure 2.14 depicts the previous equations of Shannons

Sampling Theorem. The Nyquist frequency is the frequency where foldback begins, and aliasing

becomes an issue.

26

Figure 2.14 Sampling Relationships

2.3.2 - Aliasing

Aliasing is exactly what the term implies; multiple signals can be characterized by a single set of

data points, i.e. has an alias or another name. Figure 2.15 shows a high frequency sine wave that

is characterized by the same data points that were digitized from the low frequency sine wave.

Figure 2.15 Aliasing Example

Figure 2.16 depicts the frequencies that will be aliased back to frequency of interest.

27

Figure 2.16 Aliasing Contribution

The Dataq DI-720-EN does not contain any anti-aliasing filters, so care must be taken when

logging real world sensor signals. A filter must be added to all sensor signals to reduce the

frequency content above the maximum frequency of interest. The random data that was logged

for this thesis was passed through a basic RC low pass filter which was all that was available in

the 12 VDC environment of the moving vehicle.

28

Chapter 3 Applications

3.1 - Lab Testing

The 2004 University of Cincinnati Formula SAE car was used as the test vehicle

throughout the evaluation of the data acquisition system. For the initial tests, a pink noise

generator with a rudimentary 200 Hz filter provided the signal to be measured on 16

single-ended channels. An anti-aliasing filter was added to the output of the pink noise

generator to limit the frequency content; this was controlled by a cascaded pair of

resistor-capacitor (RC) filters tuned to 200 Hz. A single RC filter would reduce the

signal by about 6 dB per octave; with the addition of a second, cascaded RC filter, the

signal is reduced another 6 dB per octave to a total of 12 dB per octave. The second set of

tests measured a 50 Hz sine wave, again on 16 single-ended channels. For the third set of

tests the Bearcat Motorsports vehicle was instrumented according to Table 3.1.

Description Sensor Type Left Front Bellcrank Travel Rotary Potentiometer Right Front Bellcrank Travel Rotary Potentiometer Left Rear Bellcrank Travel Rotary Potentiometer Right Rear Bellcrank Travel Rotary Potentiometer Throttle Position Rotary Potentiometer Front Lateral Acceleration Capacitive Discharge Accelerometer Front Longitudinal Acceleration Capacitive Discharge Accelerometer Front Vertical Acceleration Capacitive Discharge Accelerometer Rear Lateral Acceleration Capacitive Discharge Accelerometer Rear Longitudinal Acceleration Capacitive Discharge Accelerometer Rear Vertical Acceleration Capacitive Discharge Accelerometer

Table 3.1 Vehicle Sensors

All of these sensors were then used to observe the vehicle during a test on a MTS four

post road simulator. The test vehicle provided the power for the Dataq DI-720-EN, a

pink noise generator, accelerometers, and a sine wave generator all from the standard 12

29

volt motorcycle battery. The game adapter and potentiometers required a 5 and 10 volt

regulated power source. The system was tested using four different types of data

transmission, the standard parallel cable transmission, Ethernet cable transmission or

local area network, wireless local area network transmission, and wireless local area

network transmission with a parabolic antenna, which concluded the lab testing. Table

3.2 contains the details of each lab test setup.

Transfer Method Signal Setup

Parallel LAN WLAN WLAN-

ant Random 200 Hz

Sine 50 Hz

Car

1

2

3

4

5

6

7

8

9

10

Table 3.2 Lab Test Setups

3.1.1 - Parallel Port:

Dataq DI-720-EN

Random Noise

Figure 3.1 Parallel Port Testing Block Diagram

30

The parallel port data transfer method, while not the current standard, is still used in

industry today. The purpose of testing this type of data transfer was to provide a standard

to measure the performance of all other data transmission techniques. The current

method of data transfer in most data acquisition systems is through either a firewire

interface (IEEE 1394) or an Internet interface, both of which provide very fast data

transfer speeds, in comparison to the parallel port method. The block diagram in Figure

3.1 describes the test setup used for the parallel port testing. The DI-720-EN has two

methods of transferring data, parallel port or Ethernet. Parallel port communications

have four different operating modes standard; bi-directional, enhanced parallel port

(EPP), and extended capabilities port (ECP). The mode of communication depends upon

the age and sophistication of the data acquisitions and computers parallel ports. The

extended capabilities mode of communication was used to take the parallel port data used

throughout this document. The throughput sampling frequency (sample rate times the

number of enabled channels) used throughout the testing was 16,000 samples/second, so

this type of communications limit was not approached. Dataq has tested the throughput

of these modes of communication for the DI-720-EN; the results can be seen in Table

3.3.

Data Transfer Mode Maximum Data Throughput

Standard 40,000 Samples/second Bi-directional 80,000 Samples/second

EPP 250,000 Samples/second ECP Not Tested

Ethernet 50,000 Samples/second Wireless Ethernet 42,000 Samples/second

Table 3.3 Data Transfer Throughput

31

The frequency response functions (FRFs) were computed, assuming the input was logged

on channel one. The FRFs were plotted in Figure 3.2, below, and show that each channel

was logging the same signal. This can be determined by three methods, observation of

the FRF, coherence, and the time history. Observation of the FRFs shows the magnitude

to be nearly one, up to the point where the filter starts rolling off. The coherence was

analyzed as a check; it also has a magnitude of one up to about half the filter cut off

frequency, Figure 3.3. Since there was only one signal considered the input, this is really

ordinary coherence. This was attributed to the rudimentary filtering method, and a more

robust filter would extend the point of initial roll off. Figure 3.4 shows the time history

of all the channels. Because the DI-720-EN is a multiplexed unit, the time history from

channel to channel will not lay on top of one another without adjusting for the

multiplexer. Randomly generated data is not the best suited signal for observing this, or

determining the phase shift, so discussion of the phase shift has been delayed until sine

wave data has been discussed and documented. This should be discussed here since the

phase shift for a multiplexed system is a good way to document the actual time/phase

relationship for the multiplexer.

32

0 50 100 150 200 250 300 350 400 450 500-200

0

200Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.2 Parallel Port Frequency Response Functions

H1 Ordinary Coherence -- On Each Output

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Frequency (Hz)

Coh

eren

ce

Figure 3.3 Typical Coherence

33

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time

Vol

t

Time History

Figure 3.4 Parallel Port Random Time History

3.1.2 - Local Area Network:

Figure 3.5, seen below, depicts the system setup utilized for the testing performed

throughout this section, with the data recorded block alternating between random noise,

sine wave, and vehicle sensors.

Random Noise

Figure 3.5 Wired Ethernet Block Diagram

Dataq DI-720-EN

Linksys Wireless

34

The wired local area network data transfer method was the next step in the process. The

goal was to determine if a local area network would affect the data being transferred. A

data acquisition box being used as network device is a concept still just emerging in the

commercial data acquisition market. The University of Cincinnati Structural Dynamics

Research Lab (UC-SDRL) has been working with a developmental network device called

the Digital Sensor System (DSS) from Larson Davis. UC-SDRL has had success using

the DSS as a network device in vibrations testing, so logging data over a local area

network was not expected to cause any issues. Three different forms of data logged over

the local area network were 200 Hz limited random noise, 50 Hz sine wave, and vehicle

data excited by a road simulator. The 200 Hz random noise and 50 Hz sine wave were

logged using the parameters seen in Table 3.4, while the logging parameters for the

vehicle data are shown in Table 3.5.

Max Sample Rate 16000 Throughput Rate 16000

Channels 16 Sample Rate 1000

Table 3.4 Random & Sine Logging Parameters

Max Sample Rate 16000 Throughput Rate 16000

Channels 11 Sample Rate 1455

Table 3.5 Vehicle Logging Parameters

The random data spreads information across the entire frequency range allowing the

computation of frequency response functions to be useful. Computing the frequency

response function of a signal relative to itself yields a value of one:

( ) ( )( )

Input

OutputH = (3.1)

This allows for a simple observation of the frequency response functions to determine if

channel two, three, four, etc, was logging the same signal as channel one. Note that

while this the conceptual definition of the frequency response function, the Hv algorithm

35

from Reference [1], utilizing auto and cross power spectra between the input and

response channels, was actually used to compute the FRF. Figure 3.6 compares nicely to

Figure 3.2, so it appears local area networking (internet interface) does not adversely

affect the data being transmitted.

0 50 100 150 200 250 300 350 400 450 500-200

0

200Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.6 LAN Frequency Response Functions

A 50 Hz sine wave was logged according to the parameters in Table 3.4, above. The 50

Hz sine wave was tested for two reasons, observation and location of gaps in the time

history, and determination of the gap length. A gap is a location in the time data stream

where the data was either not digitized or not recorded during a specified time period.

Gaps occur because of three known reasons: large amounts of disk activity, large

amounts of network traffic, and lost communication in which the buffer overruns.

36

The DI-720-EN contains a 7500 sample buffer, which in combination with the

throughput rate determines how fast the data must be transferred from the DI-720-EN to

disk. Large amounts of disk activity can be eliminated by reducing multitasking,

allowing the computer to only write the information from the DI-720-EN. Reducing the

gaps due to large amounts of network traffic can be achieved with the application of a

dedicated network. The data acquisition systems software knows when a gap occurred,

but does not know how long the gap lasted. The gap information can be exported into a

modified comma separated variable format, but this modified format is not readily

compatible with Matlab. When the hardwares buffer overflows, it sends a packet with

an odd number of bytes to signify that a gap has occurred. Several runs were performed

to check for gaps in the data; the results indicated that no gaps occurred when the system

was adequately powered. The test vehicles 12 volt battery was inadvertently drained and

a significant number of gaps were seen during this low power testing. Figure 3.7 shows a

few seconds of the total 3 minutes of data collected. Notice the lack of jumps/spikes in

the waveform; this indicates that no gaps occurred in the data during that time frame.

While it is possible for the system to produce a gap exactly N times the wavelength,

where N is any integer number, it is highly unlikely. Therefore, a gap in the data should

produce a noticeable irregularity in the sine wave.

37

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time

Vol

t

Time History

Figure 3.7 LAN Sine Wave Time History

Zooming in on a particular area of Figure 3.7 will produce Figure 3.8. In this close up,

notice the shift in the wave form from channel one to channel two and so on. As

discussed earlier, this is to be expected with a multiplexed data acquisition device. The

phase shift can be calculated from either the measured time delay seen in Figure 3.8 or

from the phase plot in Figure 3.6.

38

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time

Vol

t

Time History

Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10Ch 11Ch 12Ch 13Ch 14Ch 15Ch 16

0.018 0.019 0.02 0.021 0.022 0.023

0.52

0.53

0.54

0.55

0.56

0.57

0.58

0.59

0.6

Time

Vol

t

Time History

Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10Ch 11Ch 12Ch 13Ch 14Ch 15Ch 16

Figure 3.8 LAN Sine Wave Close Up

The next portion of testing measured signals generated from vehicle movements. The

MTS four post road simulator was utilized as the method of excitation. The signal sent to

each hydraulic shaker can be characterized by the parameters seen below in Table 3.6.

Many other signals were tested, but none seemed to excite the system as well.

Front Rear Source: Random Random Shape: 1/F 1/F

RMS Amplitude: 0.8 0.8 Min Frequency: 0.4 0.4 Max Frequency: 2.5 4

Table 3.6 MTS Test File Information

Figure 3.9 shows the signals captured for the four bellcrank potentiometers. This data

serves as a baseline for the data that will be taken in later data acquisition tests when the

39

car is running with a wireless Internet interface. Notice the DC offset of the signal, this is

because the potentiometer was set with the middle of its travel roughly at the middle of

the bellcrank travel. The minimum potentiometer travel should have been set to the

minimum bellcrank travel, to optimize the dynamic range. AC coupling and analog

summing are two other methods used to remove DC offsets from signals. AC coupling is

essentially a high pass filter which corrupts data from zero to about five Hz and hence

affects many of the interesting signals in vehicle dynamics. The second option, an analog

summing amplifier, would add the negative value of the DC offset to the signal, thereby

centering the signal on zero. If the data acquisition devices ADC contained more bits

the DC offset would be less of an issue.

0 0.5 1 1.5 2 2.5 3 3.54.4

4.6

4.8

5

5.2

5.4

5.6

5.8Left Front Right FrontLeft Rear Right Rear

Figure 3.9 Bellcrank Rotation

40

3.1.3 - Wireless Local Area Network:

The testing performed throughout this section was obtained utilizing a Linksys wireless

router to transfer the data from the test vehicle to computer; the system diagram is shown

below in Figure 3.10.

Linksys Game Adapter Random Noise

Dataq DI-720-EN

Linksys Wireless

Wireless Link

Figure 3.10 Basic Wireless System Block Diagram

Wireless local area network data was logged according to Table 3.4 and Table 3.5. As

before, the random data was transformed using Fourier transform techniques, and

analyzed. The FRFs magnitude and phase plots were overlaid, shown in Figure 3.11.

The FRFs are flat with a magnitude of one up to 200 Hz, this shows that each channel

was logging nearly the same signal, because of the filter on the random noise generator

information above 200 Hz was expected to be partially corrupted. The phase linearly

increases from 0 to 200 Hz, described by:

t= (3.2)

41

0 50 100 150 200 250 300 350 400 450 500-200

0

200Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.11 WLAN Frequency Response Functions

Figure 3.12 depicts a 50 Hz sine wave that has been captured using a wireless local area

network. It was in Figure 3.12 that the first sign of a potential problem arose; notice the

jump in the sine wave at about 27.72 seconds.

42

27.68 27.7 27.72 27.74 27.76 27.78 27.8 27.82-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time

Vol

t

Time History

Figure 3.12 WLAN Sine Wave Time History

This is a gap or loss of data, which could be a major problem depending upon the type of

data and the nature of the analysis of the data that is required. Consider a vehicle moving

at 30 miles per hour and gap of one second, this equates to about 44 feet traveled or about

4.5 car lengths. The software for many motorsports data acquisition systems calculates a

track map from lateral acceleration, longitudinal acceleration, longitudinal velocity, and

elapsed time. If a gap occurs, this method of track map generation would fail in that

vicinity. If a very low frequency sine wave is always logged on one channel, the gap can

be determined by realigning the data after the spike based upon where it is expected to

be. Although it will not be known what happened during that time period, at least the

length of time or distance traveled will be known, and can be linked to a certain sector of

the course.

43

3.1.4 - Wireless LAN w/Parabolic Antenna

Figure 3.13, below, describes the test setup used throughout 3.1.4 - which discusses the

characteristics of testing with a parabolic antenna in the lab.

Linksys Game Adapter Random Noise

Dataq DI-720-EN

Cisco Access Point

Parabolic Antenna

Linksys Wireless

Wireless Link

Figure 3.13 Parabolic Testing Block Diagram

The final lab testing task was to test the wireless local area network with a parabolic

antenna. The purpose of a parabolic antenna was to focus the wireless Internet signals

and therefore, optimize the incoming and outgoing data signals between the data

acquisition system and the computer. Essentially, power is being wasted when the signal

is being sent out in all directions. Focusing the signal should increase the range and will

be discussed more elaborately in the onsite testing section. As in the previous FRFs, the

frequency response functions of Figure 3.14 have a magnitude of one up to the filter cut

off which was the limit of reliable data. The phase angle plots show similar results to

previous testing as well.

44

0 50 100 150 200 250 300 350 400 450 500-200

0

200Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.14 Parabolic Frequency Response Functions

The sine wave time history, Figure 3.15, shows that gaps still occurred with the antenna.

Upon further examination of the two data sets shows that the number of gaps was

reduced.

45

25.67 25.68 25.69 25.7 25.71 25.72 25.73 25.74 25.75-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time

Vol

t

Time History

Figure 3.15 Parabolic Sine Wave Time History

3.2 - Onsite Testing

Linksys Game Adapter Random Noise

Dataq DI-720-EN

Cisco Access Point

Parabolic Antenna

Linksys Wireless

Wireless Link

Figure 3.16 Vehicle Testing Block Diagram

46

The onsite testing was preformed with similar inputs as used in the lab and only with

wireless data transmission methods. Two types of onsite testing were performed, static

and dynamic. The static testing provided a baseline for onsite testing. The dynamic

testing was used to determine the real world range. Table 3.7, seen below, depicts the

onsite testing performed. A diagram of the parabolic antenna test setup used in this

section can be seen in Figure 3.16, above, while the non-parabolic antenna setup can be

seen in Figure 3.10.

Transfer Method Signal Location Engine

Setup WLAN

WLAN-ant

Random 200 Hz

Car Onsite (Static)

Onsite (Dynamic)

On Off

1

2

3

4

5

6

7

8

Table 3.7 Onsite Test Setups

3.2.1 - Static Testing

A variety of types of static testing were performed using either a standard wireless

antenna or a parabolic antenna. The primary signal recorded was a random 200 Hz wave,

vehicle data was also recorded. Both of these types of signals were recorded with and

without the engine running to determine if the noisy radio frequency characteristics of a

vehicle would have a significant impact. The initial static testing was performed at the

University of Cincinnati Center Hill Research Facility. The test vehicle and computer

were placed at opposite ends of the facility, a distance of 70 meters. A random signal

47

was then logged on 16 channels using the wireless local area network transmission

method. As expected, Figure 3.17 had a magnitude of one up to the filter cutoff

frequency, but interestingly above the filter cutoff is more closely matched than

previously seen.

0 50 100 150 200 250 300 350 400 450 500-10

0

10Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.17 Center Hill FRFs

Note the different shape and magnitude in the phase plot of Figure 3.17, compared to

Figure 3.11. Further investigation shows that the phase shift in Figure 3.11 was about 2

degrees and 0.3 degrees in Figure 3.17 at 100 Hz. Figure 3.18 corroborates the data seen

in Figure 3.17 with a coherence level of 0.9 for the entire frequency range. These

differences can be associated with a change in the test setup, note the difference in

maximum sample rates of Table 3.8 and Table 3.4. The difference in FRF phase and

magnitude characteristics above the low pass filter cutoff was not investigated.

48

Max Sample Rate

200,000

Throughput Rate

16,000

Channels 16 Sample Rate 1000

Table 3.8 Center Hill Logging Parameters

H1 Ordinary Coherence -- On Each Output

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Frequency (Hz)

Coh

eren

ce

Figure 3.18 Center Hill Coherence

Since the Center Hill facility was deemed too small to test the potential range of the

system, the test vehicle was taken to Raymond Walters, a University of Cincinnati

satellite campus, for more conclusive testing. It was during this phase of testing that an

erratic and unexplainable system fault occurred. The logging parameters were set as

stated in Table 3.8. The vehicle was set at one end of the parking lot and the computer

and routers at the other; then a random signal was logged for three minutes, same as the

Center Hill test. When the frequency response function magnitudes and phases were

49

plotted it became obvious that something was different or possibly wrong. Figure 3.19

shows that five of the sixteen channels logged were different, channels 9-13. The bad

channels had a different magnitude from 50 Hz and above. They also had an inconsistent

phase characteristic that was a significant difference in magnitude.

0 50 100 150 200 250 300 350 400 450 500-100

0

100Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.19 Bad Channels FRFs

The coherence of those five channels started dropping off at about 100 Hz and continued

much faster than the other channels, which can be seen in Figure 3.20, below.

50

H1 Ordinary Coherence -- On Each Output

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Frequency (Hz)

Coh

eren

ce

Figure 3.20 Bad Channels Coherence

The time history of all sixteen channels can be seen in Figure 3.21 for 1/10th of a second

of the three minutes logged. It was not possible to notice a difference in the 1/10th of a

second time history, so the first peak was magnified to show that five of the channels

were indeed not following the others. This test setup was retested in the UC-SDRL, as

well as in several additional tests, attempting to reproduce the results to no avail.

51

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Time

Vol

t

Time His tory

4 6 8 10 12 14 16

x 10-3

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

0.26

0.28

Time

Vol

t

Time History

Figure 3.21 Bad Channels

Before dynamic testing began it was decided to evaluate the level of noise to be expected

in a motorcycle engine powered race car environment. The first step was to instrument

the car with the sensors detailed in Table 3.1. The sensor signals were then logged for

three different test setups, engine not running, engine running, and engine running with

one accelerometer isolated. The test setup with the engine not running was performed

with the ECU (Engine Control Unit) power turned off, thus the throttle position sensor

was un-powered. Figure 3.22 shows one second of the throttle position, where the signal

randomly oscillates between 41 and 34 milli-volts. The possible quantization error was

12 milli-volts, as seen in Table 2.7, therefore the oscillation was from the ADC setting its

bits. Unshielded wires potentially act like antennas creating noise. Figure 3.23 shows

the noise level has increased to about 100 milli-volts, from engine vibration, power

source fluctuation, or RF noise from firing engine coils. The majority of the DC offset in

Figure 3.23 was from the sensor power.

52

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.033

0.034

0.035

0.036

0.037

0.038

0.039

0.04

0.041

0.042Throttle Position (Engine NOT Running)

Time (s)

Vol

tage

Figure 3.22 Throttle Position (Engine Off)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

3

3.5

4

4.5

Throttle Position (Engine Running)

Time (s)

Vol

tage

Figure 3.23 Throttle Position (Engine On)

53

Figure 3.24 shows the significant differences seen in magnitude between the engine

running, engine not running, and the engine revving test. Figure 3.25 compares the

acceleration of the isolated accelerometer for the three different tests. Figure 3.26 was

created to show the detail that was easily lost in the noise of Figure 3.25. The engine not

running accelerometer magnitude oscillates between about 0.925 and 0.965 volts, a

difference of 40 milli-volts which was above the level attributable to quantization error.

The vehicle was very mechanically noisy because of the rigidly mounted accelerometers,

as seen in Figure 3.25. The accelerometers could be filtered at a much lower frequency

in order to eliminate some of this noise since the primary interest in these signals is to

document the low frequency performance of the vehicle.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-5

-4

-3

-2

-1

0

1

2

3

4

5

Time (s)

Vol

tage

Typical Acceleration

Engine Reving Engine Running Engine Not Running

Figure 3.24 Typical Acceleration

54

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-2

-1

0

1

2

3

4

Time (s)

Vol

tage

Isolated Accelerometer Accleration

Isolated AcclerometerEngine NOT Running Engine Running

Figure 3.25 Isolated Accelerometer Acceleration

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.85

0.9

0.95

1

1.05

1.1

Time (s)

Vol

tage

Isolated Accelerometer Accleration

Isolated AcclerometerEngine NOT Running

Figure 3.26 Isolated Accelerometer

55

3.2.2 - Dynamic Testing

For actual vehicle data, the maximum frequency of interest was expected to be below 500

Hz, so a sampling rate of 1000 Hz was used throughout the testing. The dynamic testing

was performed at the University of Cincinnati Raymond Walters campus parking lot and

at the Transportation Research Centers Vehicle Dynamics Area (VDA), in Marysville

Ohio. The parking lot used at Raymond Walters contains several islands with small trees

that can affect the range. The VDA at the Transportation Research Center (TRC) was

used to setup a typical FSAE endurance course for testing. The VDA is a vast wide open

paved area where there are no obstructions for the RF equipment. It was common for one

or two gaps to occur per lap during the TRC testing, but one data set produced zero gaps

and several of the gaps could be attributed to a person walking between the antenna and

the car. A solution for the pedestrian traffic gaps could be a higher mounted computer

antenna. A second day of testing at Raymond Walters revealed another system fault.

Figure 3.27 shows the frequency response characteristics of the system fault, while

Figure 3.28 shows 0.1 seconds of the time history. It was quite obvious from these plots

that the magnitude of one channel was not captured accurately. Dataq was contacted

about this and the previously mentioned system fault but they were unable to provide an

answer. Since the two faults were different and no supplier answer could be provided, it

was assumed that these faults were hardware problems with the relatively low cost

system that was used for the data acquisitioning. It is advised that any future user be

aware of potential hardware problems, and therefore analyze the logged data carefully.

56

0 50 100 150 200 250 300 350 400 450 500-200

0

200Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.27 Bad Channel FRF

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time

Vol

t

Time History

Figure 3.28 Bad Channel Time History

57

The 200 Hz random waveform was logged once more on sixteen channels with the FSAE

racecar driving around a small course at Raymond Walters. Even though the course was

small, five gaps still occurred, which were linked to the small trees in the middle of the

course. The phase and magnitude are plotted below in Figure 3.29, this figure was very

typical for what was considered a good frequency response function plot.

0 50 100 150 200 250 300 350 400 450 500-200

0

200Block size = 1024 :Uniform Window P000 :Overlap = None :Cycles ave. = None

Pha

se (

Deg

rees

)

FRF Hv -- Input 1 On Each Output

0 50 100 150 200 250 300 350 400 4500.5

1

1.5

Frequency (Hz)

Am

plitu

de X

/F

Figure 3.29 Dynamic Frequency Response Functions

58

Chapter 4 Conclusions & Future Work

Motorsports is highly competitive industry where a minute increase in performance could

mean the difference between first and second place. An engineer in this type of

environment must utilize every tool at his fingertips at a level of proficiency that is

unrivaled. To achieve such a high level of performance from a motor vehicle a data

acquisition system is a requirement. The data acquisition methods employed in

motorsports today, include the use of a complex data logger and several sensors,

positions, accelerations, pressures, temperatures, and strains to name a few. Data

acquisition systems have advanced significantly since their inception, but most are still

not very affordable. The purpose of this thesis was to investigate the possibilities and

ca