The document outlines methods for signal conditioning and nonlinearity compensation in sensor systems, detailing techniques like piecewise linearization and artificial neural networks (ANNs). It discusses the implementation of a measurement system using MATLAB and LabVIEW for real-time monitoring and self-maintenance of sensors. The conclusion emphasizes a generalized multilayer ANN method for linearization and presents a virtual temperature measurement system alongside condition monitoring practices.

2. 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.

3. 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

4. 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.

5. End Point Linearity
dv
% nonlinearity = (dv×100)/Vfs

6. Best Fit Straight Line

7. PIECEWISE LINEARIZATION

8. LINEARIZATION TECHNIQUES

9. DIODE BASED PIECEWISE LINEARIZATION CIRCUIT

10. Case-1-When the input voltage is less than Va +
drop across D1
1
1
R
Rf
A 

11. 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||1
1
RR
Rf
A 

12. CASE-3-WHEN THE INPUT VOLTAGE
BECOMES MORE THAN THE DROP ACROSS
RA + RB AND DIODE D2
3||2||1
1
RRR
Rf
A 

14. 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.

15. Linearization By Look-up Table

16. ARTIFICIAL NEURAL NETWORK
BASED NONLINEARITY
COMPENSATION

17. 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

18. ADALINE: Adaptive Linear Element


LMS Algorithm
b
x1
W0
W1
W2
Wn
(estimated Input)
error
Xˆ
x2
.
.
xn

19. Multilayer Neural Network

20. 20
Linearization Scheme
Sensor Inverse Model
Applied
measurand
Estimated
Measurand
(X)
(Y)
(X')
Sensor response
1

21. Modeling Methodology
The 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, …, NOr
x yNonlinear measurement
System
N = order of model
ai = coefficients that represents the characteristic of
model

22. ANN Based Inverse Model

23. Considered Sensor Situations
Sensor Non-Linearity
Ni-RTD 3%
Bridge-RTD 11%
Bridge-Thermistor 16%
Thermistor 51%

24. Sensor
Percent
age
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

25. 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 linearization
2. Inverse modeling using MLP

26. Sensor
Percent
age
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

27. 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.

28. Experimental Setup For Online
Measurement
Vi
RTH
R=1Kohm
To DAQ
Hardware
DAQ
Device
LabVIE
W
Vo
+
-
Real Time
Data File

29. Block Diagram For Thermistor Resistance
Measurement VI

30. Front Panel For Thermistor Resistance
Measurement VI

31. Block Diagram For Testing Of ANN Model

32. Front Panel For Testing Of ANN Model

34. 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

35. Virtual Implementation of a
Measurement System

36. 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

37. Implementation Of Sensor Data Simulator
Module

38. 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

39. Implementation Of Voltage To Thermistor
Resistance Converter Module

40. Front Panel Of Voltage To Thermistor
Resistance Converter Module

41. Calibration And Presentation Module
The calibration module implements following expression:-
T = /[{ln(Rth/ R0)}+ /T0]
Where
Rth Thermistor resistance at T (K)
T Thermistor temperature (K)
R0 Resistance at T0 (K)
 Thermistor characteristics constant (K)

42. Calibration And Presentation Module

43. Integrated Block Diagram

44. The Front Panel Of The Developed
Application

45. 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.

46. Implementation of logic to define
sensitivity range

47. LOW AND HIGH TEMPERATURE
INDICATORS

48. Condition Monitoring and Self
Maintenance

49. INCIRCUIT CONDITION MONITORING OF
DIFFERENT CAPACITORS

50. FRONT PANEL VI

51. INCIRCUIT CONDITION MONITORING OF LIFE
LIMITING COMPONENTS IN POWER CONVERTER

52. FRONT PANEL VI

53. WEB BASED CONDITION MONITORING

54. IN-CIRCUIT SELF MAINTENANCE AND MONITORING
MODE OPERATION OF CAPACITORS

55. FRONT PANEL VI

56. BLOCK DIAGRAM VI

57. WEB BASED CONDITION MONITORING
[MONITORING MODE]

58. WEB BASED CONDITION MONITORING [SELF
MAINTENANCE MODE]

59. 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

60. • 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. 61