Signal conditioning & condition monitoring using LabView by Prof. shakeb ahmad khan
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Transcript of 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
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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]
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• 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.
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