Signal conditioning & condition monitoring using LabView by Prof. shakeb ahmad khan

SIGNAL CONDITIONING & CONDITION MONITORING

USING LABVIEWBy

Dr Shakeb A KhanProfessor

Department Electrical EngineeringJamia Millia Islamia

New Delhi

Presentation Outline

• Signal Conditioning: An Introduction• Sensor nonlinearity representation• Nonlinearity compensation techniques• Analog & Digital Techniques• ANN based technique

•ADALINE based network• MLNN based network• Implementation of trained MLNN for real time application.• Virtual implementation of a measurement system.• Labview based condition monitoring and self maintenance.• Conclusion.

Sensor Signal Conditioning

• Operations performed on sensor signals to compensate the imperfections present and to make them compatible for interface with next stage elements.

Important Signal Conditioning Issues:• Signal level & bias adjustment• Linearization• Conversions• Filtering & impedance matching• Loading• Imperfection Compensation

Significance Of Linear Response Characteristics

With linear response characteristic, resultant measurement requires minimum no. of calibration data points.

With linear response characteristic resultant measurement system will have single sensitivity value and it will be easier in this case to make the instrument direct reading type.

End Point Linearity

dv

% nonlinearity = (dv×100)/Vfs

Best Fit Straight Line

PIECEWISE LINEARIZATION

LINEARIZATION TECHNIQUES

DIODE BASED PIECEWISE LINEARIZATION CIRCUIT

Case-1-When the input voltage is less than Va + drop across D1

11

RRfA

CASE-2-WHEN THE INPUT VOLTAGE BECOMES MORE THAN THE DROP ACROSS RA AND DIODE D1 BUT IS LESS THAN THE DROP ACROSS RA + RB AND DIODE D2

2||11

RRRfA

CASE-3-WHEN THE INPUT VOLTAGE BECOMES MORE THAN THE DROP ACROSS RA + RB AND DIODE D2

3||2||11

RRRRfA

Linearization By Equation Inversion

Consider a transducer, that converts pressure into voltage as: V=K [p]^0.5 V is converted into a binary no. by ADC. DV varies as [p]^0.5. Squaring this DV p varies as DV*DV Thus a program would input a sample DV and multiply it by

itself.

Linearization By Look-up Table

ARTIFICIAL NEURAL NETWORK BASED NONLINEARITY

COMPENSATION

Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, processes the information.

The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working to solve specific problems.

Artificial Neural Networks

ADALINE: Adaptive Linear Element

LMS Algorithm

b

x1

W0

W1

W2

Wn

(estimated Input)

error

X̂

x2..xn

Multilayer Neural Network

20

Linearization Scheme

Sensor Inverse Model

Applied measurand

Estimated Measurand

(X)(Y) (X')

Sensor response

1

Modeling MethodologyThe inverse response of nonlinear measurement system may

be represented by power series expansion

x = a0 + a1 y + a2 y2 + a3 y3+ … x = ∑ ai yi; i = 1, 2, …, N Or

x yNonlinear measurement System

N = order of model

ai = coefficients that represents the characteristic of model

ANN Based Inverse Model

Considered Sensor Situations

Sensor Non-Linearity

Ni-RTD 3%

Bridge-RTD 11%

Bridge-Thermistor 16%

Thermistor 51%

Sensor

Percentage

Non-linearity

 Percentage Lowest Asymptotic RMS Error

Number of Training Data Points

 2nd Order model

 3rd Order Model

 03

 05

 07

 09

 11

 03

 05

 07

 09

 11

Ni-RTD (0 – 1800C)

 3

 0.24

 0.21

 0.19

 0.18

 0.17

 0.24

 0.19

 0.15

 0.12

 0.087

RTD-bridge(0 – 1800C)

 11

 2.9

 1.68

 1.3

 1.03

 1.0

 0.69

 0.51

 0.44

 0.43

 0.41

 Thermistor-

bridge (0 - 500C)

 16

 3.94

 2.25

 2.22

 2.02

 1.99

 3.5

 1.71

 1.42

 0.94

 0.88

 Thermistor (0-120 0C)

 51

 22.35

 12.37

 12.09

 10.97

 10.6

 16.67

 11.01

 10.41

 8.58

 8.26

Limitation Of ADALINE Model

In the case of Thermistor characteristics having 51% nonlinearity, the ADALINE model is not capable of reducing the error below 8.26%.

Proposed solutions;1. Piecewise linearization2. Inverse modeling using MLP

Sensor

Percentage

Non-linearity

 Percentage Lowest Asymptotic RMS Error

Number of Training Data Points

 2nd Order model

 3rd Order Model

 03

 05

 07

 09

 11

 03

 05

 07

 09

 11

 Thermistor (0-30 0C)

 16.5

 4.01

 3.56

 1.79

 1.69

 1.62

 3.14

 1.76

 0.96

 0.66

 0.52

 Thermistor (30-70 0C)

 17

 4.27

 3.68

 1.2

 1.16

 1.12

 2.93

 1.9

 0.96

 0.57

 0.45

 Thermistor (70-120 0C)

 17.5

 4.12

 3.38

 1.31

 1.16

 1.04

 3.33

 2.00

 0.95

 0.88

 0.85

Multi Layer Perceptron (MLP) Based Model

• Needs powerful and costly device for stand alone implementation for real time applications.• Computer based implementation is proposed for this alternative .• Proposed computer based measurement system comprises two implementation steps;

1. Offline training using MATLAB®.2. Implementation of trained network in real time

using DAQ card and LabVIEW® software.

Experimental Setup For Online Measurement

Vi

RTH

R=1Kohm

To DAQHardware

DAQDevice

LabVIEW

Vo

+

-

Real Time Data File

Block Diagram For Thermistor Resistance Measurement VI

Front Panel For Thermistor Resistance Measurement VI

Block Diagram For Testing Of ANN Model

Front Panel For Testing Of ANN Model

Percentage Error Between Actual And Estimated Temperature

Actual temperature Estimated Temperature %age Error

5 5 0

6 6 0

8 7.8334 2.08

10 10.46 4.6

15 14.8765 0.82

20 20.07 0.34

25 25.1136 0.45

30 30.5543 1.8

32 32.698 2.1

35 35.0141 .04

40 40.248 0.62

45 45.2882 0.64

50 50.2 0.4

55 54.67 0.6

60 59.56 .733

65 65.33 .507

68 67.7118 0.42

Virtual Implementation of a Measurement System

Sensor Data Simulator Module

This module represents following part of the circuit, which comprises sensor and signal conditioning circuit.

Temperature range: 250C to 650C

Corresponding voltage range (Signal Conditioning Circuit Output): 0.45 V to 1.45 V

Implementation Of Sensor Data Simulator Module

Voltage To Thermistor Resistance Converter Module

In this module following equation is implemented;

Rth =((Vi – V0) / V0 ) * Rs

Where;

Rth – Thermistor Resistance

Vi – Input voltage (= 5 V)

Rs – Series resistance (= 1 K-ohm)

V0 – Voltage across Rs

Implementation Of Voltage To Thermistor Resistance Converter Module

Front Panel Of Voltage To Thermistor Resistance Converter Module

Calibration And Presentation Module

The calibration module implements following expression:-T = /[{ln(Rth/ R0)}+ /T0]

WhereRth Thermistor resistance at T (K)T Thermistor temperature (K)R0 Resistance at T0 (K) Thermistor characteristics constant (K)

Calibration And Presentation Module

Integrated Block Diagram

The Front Panel Of The Developed Application

THE ALARM MODULE

When the measured temperature is within the range, the program presents the instantaneous value of temperature and average temperature as well.

When the temperature value is above the upper boundary (60C) then violation will be indicated by red indicator and if temperature value is less than lower boundary (30C) then violation will be indicated by green indicator as shown in fig. below.

Implementation of logic to define sensitivity range

LOW AND HIGH TEMPERATURE INDICATORS

Condition Monitoring and Self Maintenance

INCIRCUIT CONDITION MONITORING OF DIFFERENT CAPACITORS

FRONT PANEL VI

INCIRCUIT CONDITION MONITORING OF LIFE LIMITING COMPONENTS IN POWER CONVERTER

FRONT PANEL VI

WEB BASED CONDITION MONITORING

IN-CIRCUIT SELF MAINTENANCE AND MONITORING MODE OPERATION OF CAPACITORS

FRONT PANEL VI

BLOCK DIAGRAM VI

WEB BASED CONDITION MONITORING [MONITORING MODE]

WEB BASED CONDITION MONITORING [SELF MAINTENANCE MODE]

59

• Sensor based measurement systems are

discussed.• Different signal conditioning based issues are

discussed.• Reported Analog and Digital techniques for

nonlinearity compensation are described.• ANN based nonlinearity compensation

technique is presented.• Guidelines are established for selecting order of

model & optimal number of training data points for different degrees of sensor nonlinearity;

CONCLUSION

• A generalized multilayer ANN based method for sensor linearization and compensation has been presented.

• Presented real-time implementation of scheme in using NI PCI-6115 DAQ card and Labview® software.

• Total virtual implementation of temperature measurement system is presented.

• Implementation of Labview® based in-circuit condition monitoring of Electrolytic capacitor and MOSFET is discussed.

• Presented implementation of Labview® based Real-time condition monitoring and maintenance of Electrolytic capacitor.

61