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Development of ANN-based virtual fault detector for Wheatstone bridge-oriented transducers

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3 Author(s)
A. P. Singh ; Dept. of Electr. & Instrum. Eng., Sant Harchand Singh Longowal Central Inst. of Eng. & Technol., Punjab, India ; T. S. Kamal ; S. Kumar

This paper reports on the development of a new artificial neural network-based virtual fault detector (VFD) for detection and identification of faults in DAS-connected Wheatstone bridge-oriented transducers of a computer-based measurement system. Experimental results show that the implemented VFD is convenient for fusing intelligence into such systems in a user-interactive manner. The performance of the proposed VFD is examined experimentally to detect seven frequently occurring faults automatically in such transducers. The presented technique used an artificial neural network-based two-class pattern classification network with hard-limit perceptrons to fulfill the function of an efficient residual generator component of the proposed VFD. The proposed soft residual generator detects and identifies various transducer faults in collaboration with a virtual instrument software-based inbuilt algorithm. An example application is also presented to demonstrate the use of implemented VFD practically for detecting and diagnosing faults in a pressure transducer having semiconductor strain gauges connected in a Wheatstone bridge configuration. The results obtained in the example application with this strategy are promising.

Published in:

IEEE Sensors Journal  (Volume:5 ,  Issue: 5 )