ae547 2 introduction

8/10/2019 AE547 2 Introduction

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Manometers

Pitot tubes

Thermocouples o w re sys ems

a. Anemometers

b. Probes

- Simple- Slented

- Cross-wire

LDA (Laser Doppler Anemometry)

PIV (Particle Image Velocimetry)

----------------------------------------------

Data Acquisition System


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Typical PC-Based DAQ System

Transducers

Signal conditioning

DAQ hardwareSoftwarehttp://zone.ni.com/


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Data Acquisition (DAQ) Fundamentals

Data acquisition involves gathering signals from measurement sources

and digitizing the signal for storage, analysis, and presentation on a PC.

Data acquisition (DAQ) systems come in many different PC technology formsor grea ex y w en c oos ng your sys em.

Scientists and engineers can choose from PCI (Peripheral Component

, , , , , ,

Ethernet data acquisition for test, measurement, and automation applications.

system :

Transducers and sensors

Signal conditioning

DAQ hardware


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http://zone.ni.com/devzone/cda/tut/p/id/4811

PXI is the open, PC-based platform for test, measurement, and control

PXI systems are composed of three basic components chassis, systemcontroller, and peripheral modules

Standard 8-Slot PXI Chassis

Embedded System Controller andSeven Peripheral Modules


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A t ical DAQ s stem with National Instruments SCXI si nal conditionin

accessories


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Transducers and sensors

rans ucer s a ev ce a conver s a p ys ca p enomenon n o ameasurable electrical signal, such as voltage or current.

the transducers to convert the physical phenomena into signals measurable

by the DAQ hardware.

Phenomenon Transducer

Temperature Thermocouple, RTD, Thermistor Light Photo Sensor

Sound Microphone

Force and Pressure Strain Gage

Piezoelectric Transducer

Position and Displacement Potentiometer, LVDT, Optical Encodercce era on cce erome er

pH pH Electrode


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Signals

The a ro riate transducers convert h sical henomena into measurable

signals.

However different si nals need to be measured in different wa s. For this

reason, it is important to understand the different types of signals and their

corresponding attributes.

Signals can be categorized into two groups:

Analog

Digital


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Analog Signals

.

signals include voltage, temperature, pressure, sound, and load. The three primary

characteristics of an analog signal include level, shape, and frequency

Because analog signals can take on any value, level gives vital

information about the measured analog signal. The intensity of a

light source, the temperature in a room, and the pressure insidea c am er are a examp es a emons ra e e mpor ance o

the level of a signal.

Primary Characteristics of an Analog Signal


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Digital Signals

A digital signal cannot take on any value with respect to time.Instead, a digital signal has two possible levels: high and low.

Digital signals generally conform to certain specifications that define characteristics

of the signal. Digital signals are commonly referred to as transistor-to-transistor logic

(TTL). TTL specifications indicate a digital signal to be low when the level falls within

0 to 0.8 V, and the signal is high between 2 to 5 V.

The useful informationthat can be measured

includes the state (on or

off, high or low ) and the

rate of a di ital how thedigital signal changes

state with respect to time


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Signal Conditioning

Sometimes transducers generate signals too difficult or too dangerous tomeasure directly with a DAQ device.

For instance, when dealing with high voltages, noisy environments, extreme high

and low signals, or simultaneous signal measurement, signal conditioning is

essential for an effective DAQ system. Signal conditioning maximizes the

accuracy of a system, allows sensors to operate properly, and guarantees safety.

Signal conditioning accessories can be used in

a variety of applications including:

AttenuationIsolation (The system being monitored may

-

damage the computer without signal

conditioning)

Simultaneous sampling

Sensor excitation

Multi lexin A common techni ue for

measuring several signals with a singlemeasuring device is multiplexing.)


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Signal

conditioning


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DAQ Hardware

.

It primarily functions as a device that digitizes incoming analog signals so that the

computer can interpret them. Other data acquisition functionality includes:

Analog Input/Output

Digital Input/Output

Counter/Timers Multifunction - a combination of analog, digital, and counter operations on a single

device

NI Wi-Fi Data Acquisition


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CPU

Central Processing

101011

Unit

Address ControlData

A/D

ConverterSampled Analog

Input Signal Digital Output

Input

Device

Keyboard

Disk

A/D Converter

111

Buss

110

101Digital

OutputMemory

010

011

Output

Device

CRT

Printer

Disk

000

001

0 Full

Scale1/2 LSB

Computer monitors

na og npu

LSB: least significant bitRef: http://cobweb.ecn.purdue.edu/~aae520/


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Full scale volta e=23range R=8V


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Dynamic Response of


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A static measurement of a physical quantity is

time.

The deflection of a beam under a constant load

deflection would vary with time (dynamic


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- - -,

A system may be described in terms of a general

var a e x t wr tten n erent a equat on orm as:

where F(t) is some forcing function imposed on the system.

.

A zeroth-order system would be governed by:


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A first-order system is governed by:

A second-order system is governed by:


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The zeroth order system indicates that the system variable x(t) will

follow the input forcing function F(t) instantly by some constant

value:

e cons an a0 s ca e e s a c sens v y o e sys em.


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The first order system may be expressed as:

The = a1/a0 has the dimension of timean s usua y ca e e me cons an

of the system.


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For step input : F(t)=0 at t=0F(t)=A for t>0

Along with the initial condition x=x at t=0

The solution to the first order system is:

where

Steadystate

Transientresponse of

(call x)

The same solution can be written in dimensionless forms as:


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Dynamic Response of Measurement

Systems

ero r er ys em:

Input signalOutput signal

)()( tFKtx =


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npu s gna examp es:

- 0

Unit step function

(Heaviside function)Shifted unit step function


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dx=

Systems

Step response

-

dt

Impulse response

)1()0( /teFKx =

/)0( teFKx =11

5.=

ude

0.6

0.70.8

.

0.6

0.7

0.8

0.9

litude

1=2=

Amplit

0.3

0.4

0.5

2=0.3

0.4

0.5Am

0 2 4 6 8 100

0.1

.

/t

1=

5.=

/t0 2 4 6 8 10

0

0.1

.


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First Order S stemInput OutputSystems

Sinusoidal Response )sin(1 22

++

= tx

s n=

-

tan =

Amplitude Decrease and Phase Shift

0.6

0.8

1

-20

-10

0

GaindB

Input

-0.2

0

0.2

.

amplitude

10-1

100

101-30

0

deg

Output

-0.8

-0.6

-0.4

-1 0 1

-

-60

-90

Phase

0 0.5 1 1.5 2 2.5-1

time

Bode Plot


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Systems

Step response - Second Order System

1.8

2

22

2

dxxd =

1.2

1.4

.

ude

-

frequencynatural-

2

n

nnndtdt

0.6

0.8

1

Am

plit

res onsefastestfor-7.

nsoscillationo-dampingcritical-1

=

=

0.2

0.4

dam ed criticallfortimethehalfin

valuestaticof5%tocomesSystem

overshoot5%

0 5 10 15 20tn

systems


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Second Order S stem - Sinusoidal Res onseSystems

=+

==

2

1

22

2

1

2

tan)sin()0(

x)sin()0( ntFK

tFF

n

nn

(dB)

ase

(deg)

n/


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Systems

econ r er ys em - mpu se esponse

)1sin()0( 2 += teFKx

n

tn

0.8

1Impulse response - Second Order System

0.4

0.6

e

-0.2

0

0.2

Amplitu

-0.6

-0.4

0 20 40 60 80 100-0.8

time


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1

1 .2

1

1 .2

0 .2

0 .4

0 .6

.

AmplitudeRatio

0 .2

0 .4

0 .6

.

AmplitudeRatio

0 1 0 2 0 3 0 4 0 5 00

F r equenc y ( H z )

0 1 0 2 0 30

F r equenc y ( H

Low Pass Filter High Pass Filter

Removes High Frequency Noise

(Such as 60, 120 Hz)

0 .8

1

1 .2

eR

atio 0 .8

1

1 .2

eR

atio

0

0 .2

0 .4

.

Amplitud

0

0 .2

0 .4

.

Amplitud

F re q ue nc y (H z) F re q ue nc y (H z)

Band Pass Band Stop


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Example: MUSIC

Basically, the equalizer in your stereo is nothing more than a set of band

pass filters in parallel. Each filter has a different frequency band that it

con ro s. e equa zer s use o a ance e s gna over eren

frequencies to shape the noise (music)

amplitude

Dr. Peter Avitabile University of Massachusetts Lowell

frequency


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The instrument that is used to make measurements will have some verydefinite frequency characteristics. This defines the usable frequency range

of the instrument. As art of the lab and measurements taken there was a

different usable frequency range for the oscilloscope and the digital

multimeter

amp u e amp u e



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amplitude amplitude


frequency requency


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In addition to instruments, the actual transducers used to makemeasurements also have useful frequency ranges. For instance, a strain

gage accelerometer and a peizoelectric accelerometer have different useful

frequency ranges

amp u e amp u e



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Fs = 100;t = 0:1/Fs:1;

* * * * * * *

. .

% DC plus 5 Hz signal and 40 Hz signal sampled at 100 Hz for 1 sec

2

1.5Total Signal

DC Level

0.5

plitude(volts)

Signal

-0.5

0A g requency

0 0.2 0.4 0.6 0.8 1-1

Time (sec)

http://cobweb.ecn.purdue.edu/~aae520/


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1 .5

2

Original1

1.2

- 1

- 0 . 5

0

0 .5

1

Amplitud

e(volts) Signal

Filter

Filtfilt0.2

0.4

0.6

0.8

AmplitudeRatio

Low Passecovers

DC + 3Hz

. . .T i m e ( s e c )

0 .5

1

1 .5

2

de(vo

lts)

0 10 20 30 40 50Frequency (Hz)

0.8

1

1.2

eRatio

Cheby2

High Pass Recovers

0 0 . 2 0 . 4 0 . 6- 1

- 0 . 5

0

T i m e ( s e c )

Amplit

2

0 10 20 30 40 500

0.2

0.4

.

Frequency (Hz)

Amplitud

1 2

- 0 . 5

0

0 .5

1

1 .5

Amplitude(volts)

0.4

0.6

0.8

1

.

AmplitudeRatio Cheby2Band Pass Recovers

3Hz

0 0 . 2 0 . 4 0 . 6- 1 . 5

- 1

T i m e ( s e c )

1 .5

2

0 10 20 30 40 500

0.2

Frequency (Hz)

1

1.2

- 1

- 0 . 5

0

0 .5

1

Amplitude(volts)

0.2

0.4

0.6

0.8

AmplitudeRatio

Cheby2

Stop Band

ecovers

DC + 40Hz

. . .T i m e ( s e c )0 10 20 30 40 50

Frequency (Hz)


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easuremen rror

True Value

as rror

Systematic Error

Remains Constant During Test

i

Bias Error

Calibration

or judgement

Random ErrorPrecision ( Random Error )Precision Index - Estimate ofxxii =

Total Error

Deviation

A statistic, s, is calculated

from data to estimate the recisionerror and is called the precision

index

N

i = + i

11

= N

xx

s

i

We may categorize bias into five classes :


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o large known biases,

o small known biases,

o large unknown biases and

o small unknown biases that may have unknown sign () or known sign.

The large known biases are eliminated by comparing the instrument to a standard

instrument and obtaining a correction. This process is called calibration.

Small known biases may or may not be corrected depending on the difficulty of the

correction and the magnitude of the bias.

The unknown biases, are not correctable. That is, we know that they may exist but we do

not know the sign or magnitude of the bias.

Five types of bias errors


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.

The introduction of such errors converts the controlled measurement process

into an uncontrolled worthless effort.

Large unknown biases usually come from human errors in data processing,

incorrect handling and installation of instrumentation, and unexpectedenvironmental disturbances such as shock and bad flow profi les. We

must assume that in a well controlled measurement process there are no

large unknown biases. To ensure that a controlled measurement process

exists, all measurements should he monitored with statistical quality control

charts.

Measurement Error


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Precise, Accurate (Unbiased) Precise, Inaccurate (Biased)

Im recise Accurate Unbiased Im recise Inaccurate Biased


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Instrument

Readin s

Truea ue

&precise

butprecise

butaccurate

&imprecise


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arame er s ma on

A desirable criterion in a statistical estimator is unbiasedness. A statistic is

unbiased if the expected value of the statistic is equal to the parameter being

estimated.

Unbiased estimators of the parameters, , the mean, and , the standarddeviation are:

xN

i Estimation of meanN

x=

[ mean(data) ]

)(1

2

=

xxN

i Estimation of standard deviation,

1N


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Estimate of the Probability Density Function


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Estimate of the Probability Density Function

[ hist(data,# of bins) ]

700

800

500

600

200

300

850 900 950 1000 1050 11000

100

(data measured)


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Examine the data for consistent. No matter how hard one tries, there will always be some datapoints that appear to be grossly in error. The data should follow common sense consistency, and

points that do not appear "proper" should be eliminated. If very many data points fall in the

category of "inconsistent" perhaps the entire experimental procedure should be investigated for

gross mistakes or miscalculations.

Perform a statistical analysis of data where appropriate. A statistical analysis is only appropriatewhen measurements are repeated several times. If this is the case, make estimates of such

parameters as standard deviation, etc.

Estimate the uncertainties in the results. These calculations must have been performed in

advance so that the investigator will already know the influence of different variables by the time

the final results are obtained.

Anticipate the results from theory. Before trying to obtain correlations of the experimental data,

the investigator should carefully review the theory appropriate to the subject and try to think some

information that will indicate the trends the results may take. Important dimensionless groups,

pertinent functional relations, and other information may lead to a fruitful interpretation of the data.

Correlate the data. The experimental investigator should make sense of the data in terms of

physical theories or on the basis of previous experimental work in the field. Certainly, the results

of the experiments should be analyzed to show how they conform to or differ from previous

investigations or standards that may be employed for such measurements.

(Ref. Holman, J. P., Experimental Methods for Engineers")

ae547 2 introduction

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