Lecture handout given by Prof. shakeb ahmad khan on Signal conditioning & condition monitoring using LabView in National Workshop on LabVIEW and its Applications.Organized at Dayalbagh Educational Institute,Dayalbagh,AGRA from 28-29 August 2015.
Signal conditioning & condition monitoring using LabView by Prof. shakeb ahmad khan
1.
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.
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
13.
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.
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
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
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
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
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
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)
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.
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.