Download A General method of design and implementation of

AN ADAPTIVE CALIBRATION
CIRCUIT FOR PRESSURE
MEASUREMENT USING
OPTIMIZED ANN
Dr. B. K. Roy, Associate Professor,
Electrical Engineering Department,
National Institute of Technology Silchar.
OUTLINE
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Capacitance Pressure Sensor
Present scenario of pressure measurement
Limitation on present techniques
Objective of proposed work
A solution using Optimized ANN
Results & Conclusion
Further improvement
Any questions ???
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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CAPACITANCE PRESSURE SENSOR
• A pressure sensor measures pressure,
typically of gases or liquids.
• Pressure sensors are used for control and
monitoring
in
thousands
of
everyday
applications.
• CPS uses a diaphragm and pressure cavity to
create a variable capacitor to detect strain due to
applied pressure.
•Changes in pressure cause it to deflect and
change the capacitance.
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CPS (CONT..)
Where
Ψ = 3(1- ρ2)R4
Φ = 16 E d3
q=r/R
ρ - Poisson ratio
E - Modulus of Elasticity
ϵr - Permittivity of dielectric material
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EXPRESSIONS USED
E (t) = Modulus of Elasticity at temperature to C
E (25) =Modulus of Elasticity at temperature 25oC
t = Temperature in o C
C (t) = capacitance at temperature t o C
C (to) = capacitance at temperature to o C
α, β = constants
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PRESENT SCENARIO OF PRESSURE
MEASUREMENT USING CPS
Process
Capacitance
Pressure
Sensor
Capacitance
to frequency
and
frequency to
voltage
converter
Calibration
circuit
Display
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Characteristic of the present technique
5
Voltage in volts
4
3
Calibration
unit
2
Display
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
Output of data conversion
unit
An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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Limitation on range
 Datasheet
suggests that only 10% to 75%
of the full scale of input range is
considered for practical measurement.
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Can pressure be measured with the same set-up
if the diaphragm is changed with diaphragm
having different modulus of elasticity ???
5
Voltage in volts
4
3
Calibration
unit
2
E = 70G
E = 100G
E = 130G
1
0
Display
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
Output of data conversion
unit
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System output i.e. pressure measurement with
different diaphragm yields inaccurate results.
5
Voltage in volts
4
3
2
E = 70G
E = 100G
E = 130G
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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Can pressure be measured with the same set-up
if the diaphragm is changed with diaphragm
having different thickness ???
5
Voltage in volts
4
3
Calibration
unit
2
h = 0.5m
h = 0.52m
h = 0.54m
1
0
Display
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
Output of data conversion
unit
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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System output i.e. pressure measurement with
different diaphragm yields inaccurate results.
5
Voltage in volts
4
3
2
h = 0.5m
h = 0.52m
h = 0.54m
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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Can pressure be measured with the same set-up
if the dielectric material is changed ???
5
Er = 30
Er = 60
Er = 90
Voltage in volts
4
3
Calibration
unit
2
Display
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
Output of data conversion
unit
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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System output i.e. pressure measurement with
different dielectric yields inaccurate results
5
Er = 30
Er = 60
Er = 90
Voltage in volts
4
3
2
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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Can pressure be measured with the same
set-up if the temperature is changed???
5
t = 25
t = 50
t = 75
Voltage in volts
4
3
Calibration
unit
2
Display
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
Output of data conversion
unit
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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System output i.e. pressure measurement with
different temperature yields inaccurate results
5
t = 25
t = 50
t = 75
Voltage in volts
4
3
2
1
0
0
0.5
1
1.5
Pressure in Pascal
2
2.5
x 10
6
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Summary on limitation of existing setup
 Calibration
needs to be repeated every time
 with the change in diaphragm thickness
 whenever there is a change in temperature
 with the change in diaphragm elasticity
modulus.
 while replacing the dielectric
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Objectives of the proposed work:
 To
provide intelligence in the measurement
technique so as to overcome the present
limitations i.e.
Linearity range needs to be extended (Obj -1)
 Same set up of measurement technique should
be capable of giving accurate measurement with
changes (within certain ranges) in diaphragm
thickness, elasticity modulus, dielectric, and
temperature. (Obj -2)
 An appropriate choice of ANN (Obj -3)

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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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Block diagram of the proposed work:
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Training, validating and testing of ANN
Input data matrix for ANN
Pressure
0.0
0.017
MPa
0.034M …
Pa
2.5
MPa
E = 70, h = 0.5mm, ℇr = 30, t = 25 oC
4.9304
4.9148
4.8991
…..
2.3073
E = 70, h = 0.5mm, ℇr = 30, t = 50 oC
4.5652
4.5507
4.5362
….
2.1364
E = 70, h = 0.5mm, ℇr = 30, t = 75 oC
4.2503
4.2369
4.2234
….
1.9891
E = 70, h = 0.5mm, ℇr = 60, t = 25 oC
3.9761
3.9635
3.9509
….
1.8607
E = 70, h = 0.5mm, ℇr = 60, t = 50 oC
4.9321
4.9183
4.9043
….
2.9162
3.9833
3.9779
3.9726
….
3.3813
Voltages
at the input with
…….
……
E = 130,h = 0.54mm, ℇr = 90, t = 75oC
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Target for ANN
Pressure
0.0
Voltages
at the input with
0.017
MPa
0.034 x … 2.5 x
MPa
MPa
E = 70, h = 0.5mm, ℇr = 30, t = 25 oC
0
0.033
0.066
E = 70, h = 0.5mm, ℇr = 30, t = 50 oC
0
0.033
0.066
E = 70, h = 0.5mm, ℇr = 30, t = 75 oC
0
0.033
0.066
E = 70, h = 0.5mm, ℇr = 60, t = 25 oC
0
0.033
0.066
E = 70, h = 0.5mm, ℇr = 60, t = 50 oC
...
…….
0
….
0.033
...
0.066
….
……
….
...
….
E = 130, h = 0.54mm, ℇr = 90, t = 75oC
0
0.033
0.066
5.00
...
...
...
...
5.00
5.00
5.00
...
5.00
….
...
….
...
5.00
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OPTIMIZATION OF NEURAL NETWORK
 An
optimized ANN is considered by comparing
various schemes, algorithms, transfer function
of neuron, and number of hidden layers based
on minimum mean square error (MSE)
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OPTIMIZATION OF VARIOUS ANN MODELS (NO
OF HIDDEN LAYERS, SCHEME, AND ALGORITHM)
Layers
AL - 1
AL - 2
AL - 3
AL - 4
1
5.6E-1
6.2E-1
0.6E-1
8.5E-2
2
2.6E-2
1.2E-2
6.8E-3
5.7E-4
3
8.2E-4
2.1E-4
4.8E-5
6.7E-5
4
3.2E-6
0.2E-6
4.8E-7
2.2E-7
5
1.1E-9
2.1E-9
6.7E-10
4.2E-10
AL - 1: linear trained Levenberg-Marquardt Algorithm
AL - 2: linear trained Guass Newton Algorithm
AL – 3: linear trained Artificial Bee Colony
AL – 4: Back Propagation neural network trained by Ant Colony Optimization
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OPTIMIZATION OF VARIOUS ANN MODELS WITH
BACK PROPAGATION SCHEME (NO OF HIDDEN
LAYERS, AND ALGORITHM)
N
H
L
BP_GNA
BP_LMA
BP_ABC
BP_ACO
BP_GA
BP_PSO
1
2.23E-2
1.68E-2
1.11E-2
9.10E-3
7.98E-3
6.25E-3
2
1.10E-3
9.55E-4
8.25E-4
6.38E-4
3.98E-4
1.02E-4
3
6.99E-5
4.08E-5
4.25E-5
3.02E-5
1.28E-5
9.87E-6
4
3.87E-6
1.14E-6
9.87E-7
7.78E-7
5.28E-7
3.14E-7
5
6.28E-7
5.11E-7
3.87E-7
4.02E-7
2.78E-7
1.97E-7
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OPTIMIZATION OF VARIOUS ANN MODELS WITH BACK
PROPAGATION SCHEME, PSO ALGORITHM, AND NO OF
HIDDEN LAYERS AS 1 AND VARYING TF OF NEURONS
Sl.n
Transfer function
o
1. Tanh
MSE
4.17E-3
2.
Sigmoid
1.78E-3
3.
Linear Tanh
2.12E-3
4.
Linear sigmoid
3.15E-3
5.
Softmax
6.14E-4
6.
Bias
3.28E-3
7.
Linear
6.25E-3
8.
Axon
9.74E-4
9.
Tansig
8.87E-4
10. Logsig
7.89E-4
An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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SUMMARY OF OPTIMIZED NEURAL NETWORK
Input
OPTIMIZED NEURAL NETWORK
Training base
Database
Validation base
Test base
No of neurons in
1st layer
1st layer
Transfer function of
Output layer
PARAMETERS
84
28
28
10
Softmax
Linear
Pressure
E
h
ϵr
Temp
0.0 Pa
70 GPa
0.50 mm
30
25oC
max 2.5 MPa 130 Gpa
0.54 mm
90
75oC
min
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SIMULATED RESULTS
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An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
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SIMULATION RESULTS
AP in E in
106 Pa GPa
0.5
0.5
0.5
1.0
1.0
1.0
1.5
1.5
1.5
2.0
2.0
2.0
2.5
2.5
2.5
70
75
80
85
90
95
100
105
110
115
120
125
130
75
80
h in
mm
ϵr
0.54
0.53
0.52
0.51
0.50
0.50
0.51
0.52
0.53
0.54
0.54
0.53
0.52
0.51
0.50
30
35
40
45
50
55
60
65
70
75
80
85
90
85
80
t in DCO ANN
oC
in V o/p in
V
25 4.99 0.998
30 4.86 0.992
35 4.19 1.001
40 3.45 2.002
45 3.06 1.993
50 2.76 1.997
55 2.38 2.998
60 2.20 2.991
65 2.05 2.994
70 1.83 4.006
75 1.71 4.008
70 1.63 4.001
65 1.48 4.997
60 1.55 4.991
55 1.25 5.003
An Adaptive Calibration Circuit for Pressure Measurement Using Optimized ANN
%
MP in
106 Pa error
0.4991
0.4993
0.5006
1.0002
0.9991
0.9996
1.4999
1.4992
1.4996
2.0006
2.0010
2.0002
2.4998
2.4993
2.5006
0.18
0.14
-0.12
-0.02
0.09
0.04
0.01
0.05
0.03
-0.03
-0.05
-0.01
0.01
0.03
-0.02
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SUMMARY OF RESULTS
 Results suggest that the following
objectives are achieved:




Full range of input scale is linearized and
made used for measurement.
Measurement technique is made adaptive
of variation in physical parameters and
thus, repeated calibration is avoided.
Temperature compensation is made by
avoiding use of another primary sensor.
An optimized ANN is used to achieve the
objectives.
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32
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