dtc project 8.5 “intelligent sensing”

Data Information FusionDefence Technology Centre

DTC Project 8.5“Intelligent Sensing”

Prof C.J.Harris and Prof N.M.WhiteDr D. Karatzas and Dr A. Chorti

University of SouthamptonSchool of Electronics and Computer Science

Data Information Fusion Defence Technology CentreProject 8.5 Intelligent Sensing

Teaching Old Sensors New Tricks

Intelligent Sensors are systems combining sensing elements and sophisticated processing in the sensor housing. They are able to process information locally and autonomously react to specific situations.


Redundancy of simple sensors is not always a good idea. In the

case of a catastrophic event they will all suffer the same damage. Intelligent Sensors on the other

hand can reconfigure themselves

Intelligent Sensors can go where

humans cannot

Humans use intuition to respond to a tricky situation. Intelligent Sensors can also respond in a

clever way!

It is desirable to employ Intelligent Sensors to keep

human beings away from hazardous situations

Advantages of Intelligent Sensors


INTELLIGENTSENSOR

(DTC Project 8.5)

DTC Project 8.5 investigated algorithms for optimal signal extraction from sensors in the context of an Intelligent Sensor Software Architecture

INTELLIGENT SENSOR(DTC Project 8.5)


INTELLIGENTSENSOR 1

(DTC Project 8.5)

SENSOR MANAGEMENT(DTC Project 8.1)

PERFORMANCE METRIC

DECISION / CONTROL

DATA FUSION SYSTEM

INTELLIGENT SENSOR N

(DTC Project 8.5)…

By processing information locally, the sensor management system (DTC Project 8.1) can assume higher confidence on reported data


State of the Art Research

IEEE1451

BS7986

Few industry standards exist for Intelligent Sensors

IEEE 1451 defines low-level aspects of intelligent sensors and communication protocols

BS 7986 describes higher levelin-sensor processing but does not define algorithmic implementation details

DTC Project 8.5 covers this gap!

Maintains compatibility to existing industry standards

Formally defines a framework for Intelligent Sensors implementation

Investigated algorithmic approaches to tackle common issues of real-world applications




Intelligent Sensor Software Architecture

A generic, modular architecture was developed as an advantageous intelligent sensor implementation

The Project 8.5 Intelligent Sensor Software Architecture introduces the following functionality:

To self-validate sensor data and identify inconsistent inputsTo self-adjust to drift/bias effects usually caused by the sensing element ageing or poisoning

To communicate sensor’s condition to the sensor management level, so proper action can be taken

To adapt to environmental changesTo autonomously reconfigure in order to continue operation despite any sensor degradation


• Self-validation• Self-adjustment to drift/bias• Communication of sensor’s condition• Adaptation to environmental changes• Autonomous reconfiguration


Intelligent Sensor

T

Primary Measurand

Environment Measurand #1

Environment Measurand #2

FaultDetection

FaultDetection

FaultDetection

…

FaultDetection

FaultDetection

…

FaultDetection

FaultDetection

…H

Intelligent SensorControl

Module Messaging

IEEE 1451 Communi-

cations

IEEE Sensor Identification

IEEE 1451TEDS

Timing

VV

VVstatus

VU

VUstatus

VDstatus

Com

mun

icat

ions

In

terf

ace

(IEEE

145

1)

Sensor ModelProvider

T

Envi

ronm

ent

Inte

rnal

Fus

ion

Inte

rnal

Fusi

onIn

tern

alFu

sion

DriftEstimation &

Compensation

Sensor Model Provider







Sensor Interface #1

Sensor Interface #2

Sensor Interface #n

…

Sensor Interface #1

Sensor Interface #n

…

Sensor Interface #1

Sensor Interface #n

…

Modules are combined in a mix-and-match fashion to create specific Intelligent Sensor implementations that address real-life scenarios

A primary measurand, as well as any number of secondary environmental measurands can be monitored in parallel

The Sensor Interface modules are responsible to:

Communicate with the sensing element hardwareObtain measurements on demandPerform basic signal processing (linearisation, A/D conversion etc)

The Fault Detection modules:

Assess incoming data and produce an uncertainty valueCorrect incoming data if possibleIndicate how the above values were calculated

The Fault Detection modules can make use of theoretical sensor models provided by Sensor Model Provider modules, which:

Act as sensor model librariesCan select the best model to use at any given time

The outputs from each array of sensors are fused by Internal Fusion modules:

Generate a single value and uncertainty at each iterationFilter out inconsistent values

The fused measurements can be checked for drift/bias by a Drift Estimation & Compensation module:

Estimates different types of drift from historical dataCorrects for drift and updates the uncertainty value accordinglyMakes use of physical sensor models

All modules are bound together by the Intelligent Sensor Control module:

Communicates the final corrected measurement to higher processesEnsures compatibility with IEEE and BS standardsIs responsible for timing, messaging between modules etc.


Internal Fusion

Each module in the architecture addresses a distinct common issue of real life implementations

But,There is no single way to tackle each of these issues!

DTC Project 8.5 investigated alternative algorithmic implementations for each of the modules

Sensor Interface

Intelligent SensorControl

Drift Estimation &Compensation

FaultDetection

Sensor ModelProvider

1. Additive Drift #12. Additive Drift #23. Multiplicative Drift4. …

Available AlgorithmicImplementations


Intelligent SensorPressure

Temperature

Sensor Interface

IntelligentSensorControl


Sensor Interface

Sensor Interface

FaultDetection

FaultDetection



VV

VVstatus

VU

VUstatus

VDstatusCom

mun

icat

ions

In

terf

ace

(IEEE

145

1)


Inte

rnal

Fus

ion

As an example, we will see how the Intelligent Sensor Software Architecture of Project 8.5 can be used to implement an Intelligent Sensor featuring a piezoresistive pressure sensor as the primary sensing element

The piezoresistive pressure sensor is interfaced with the architecture through a Sensor Interface module

The Sensor Interface module feeds directly a Drift Estimation & Compensation module which corrects the input for additive and multiplicative drift using models provided by a Sensor Model Provider module

The physical model for the pressure sensor is dependent on temperature, and the Sensor Model Provider needs to know this information to select the appropriate model

We can use an array of two temperature sensors (not necessarily of the same type), which interface with the Intelligent Sensor through their own Sensor Interface modules

The output of each of these sensors is assessed by a Fault Detection module, which makes use of physical models supplied by Sensor Model Provider modules

The temperature measurements are fused by an Internal Fusion module, before passed to the Intelligent Sensor Control moduleThe Intelligent Sensor Control is responsible to communicate temperature information back to the pressure sensors’ Sensor Model Provider moduleFinally, the corrected pressure information is passed to the Intelligent Sensor Control to communicate to higher processes


Intelligent SensorPressure

Temperature

Sensor Interface



Sensor Interface

Sensor Interface

FaultDetection

FaultDetection




Inte

rnal

Fus

ion

Intelligent Sensor

Pressure

Temperature

Sensor Interface



Sensor Interface

Sensor Interface

FaultDetection

FaultDetection




Inte

rnal

Fus

ion

On the right appears a snapshot of Project 8.5’s Intelligent Sensor Demonstrator, showing the implementation described

The correspondence of the modules between the architectural design and the implementation is highlighted

The demonstrator is now shown in action. As new measurements become available, each module performs its own processing on the data

The estimated additive and multiplicative drift can be seen in the Drift Estimation & Compensation module

The Fault Detection modules identify outliers and remove their effects on the signal


Project’s 8.5 developed a low level Intelligent Sensor Software Architecture which is generic, modular, compliant with existing industry standards and can be used to implement any sensory application

Some of the key attributes of the Intelligent Sensor Software Architecture are:

Project 8.5’s Intelligent Sensor requires minimal maintenance and therefore minimal human intervention

Its modular character makes it easy to use through a mix-and-match fashion

It is trivial to upgrade individual modules without altering the overall implementation

For each module a number of alternative implementations are available to cover a variety of application specific needs



The list of potential applications for Project 8.5’s research output is endless. Two characteristic examples stemming from active research projects in the University of Southampton are:

Potential ApplicationsPotential Applications

Biometric Keypad Southampton Artificial Hand


Potential ApplicationsBiometric Keypad Southampton Artificial Hand

Combines “Chip and PIN” technology with biometric keystroke recognition to identify or verify an individual

Intelligent Sensors can be introduced on each key (current implementation features 2 piezoelectic sensors behind each pad)

Range of real-life Applications:

Banking

Security Systems (access control)

A long-running project at the University of Southampton utilising state of the art sensor technology

Features 3 sensors on each fingertip: Force, Temperature and Slip

Relationship to Project 8.5:

Introduce Intelligent Sensors on its fingertips

Reduce cabling requirements


D. Karatzas, A. Chorti, C.J. Harris and N.M. White, “Teaching Old Sensors New Tricks: Archetypes of Intelligence”, accepted at IEEE Sensors journal (Special Issue on Intelligent Sensors)

A. Chorti, D. Karatzas, N.M. White and C.J. Harris, “Use of the EKF for state dependent drift estimation in weakly nonlinear sensors” accepted at Sensors Letters

A. Chorti, D. Karatzas, N.M. White and C.J. Harris, “Intelligent Sensors in Software: The Use of Parametric Models for Phase Noise Analysis”, submitted to ICISIP 2006 conference on Intelligent Sensing and Information Processing, IEEE

N.M. White and P.J. Boltryk, “Advances in Intelligent Sensors”, book chapter, to appear in “Adaptronics”, Springer

P.J. Boltryk, C.J. Harris and N.M. White, “Intelligent Sensors – a generic software approach”, Sensors & their Applications XIII, University of Greenwich at Medway, Chatham Maritime, Kent, September 2005

P.J. Boltryk, C.J. Harris and N.M. White, “An Algorithmic Approach to the Optimal Extraction of Signals from Intelligent Sensors”, Nanotech 2005, Anaheim, USA, May 8-12, 2005

N.M. White, “Intelligent Sensors, Systems or Components?”, Invited Paper, Nanotech 2005, Anaheim USA, May 8-12, 2005

Publications


Contact DetailsAddress University of Southampton

School of Electronics and Computer ScienceRoom 1001, Building 86Southampton, SO17 1BJ

Telephone +44 (2380) 599204

Website www.dtc.soton.ac.uk

Prof N.M.White [email protected] C.J.Harris [email protected] D.Karatzas [email protected] A.Chorti [email protected]

dtc project 8.5 “intelligent sensing”

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