Signal conditioning & condition monitoring using LabView by Prof. shakeb ahmad khan
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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.
![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.](https://image.slidesharecdn.com/prof-150901074550-lva1-app6891/85/Signal-conditioning-condition-monitoring-using-LabView-by-Prof-shakeb-ahmad-khan-14-320.jpg)
![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)](https://image.slidesharecdn.com/prof-150901074550-lva1-app6891/85/Signal-conditioning-condition-monitoring-using-LabView-by-Prof-shakeb-ahmad-khan-41-320.jpg)
![WEB BASED CONDITION MONITORING
[MONITORING MODE]](https://image.slidesharecdn.com/prof-150901074550-lva1-app6891/85/Signal-conditioning-condition-monitoring-using-LabView-by-Prof-shakeb-ahmad-khan-57-320.jpg)
![WEB BASED CONDITION MONITORING [SELF
MAINTENANCE MODE]](https://image.slidesharecdn.com/prof-150901074550-lva1-app6891/85/Signal-conditioning-condition-monitoring-using-LabView-by-Prof-shakeb-ahmad-khan-58-320.jpg)
Signal conditioning & condition monitoring using LabView by Prof. shakeb ahmad khan
- 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
- 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
- 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
- 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