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

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.