This paper presents a comparative evaluation of two classification schemes that can be used to accurately diagnose the health of machines in the presence of sensor failure. In the developed approach, multiple sensors acquire vibration and sound signals from a machine and the signals are represented using the Wavelet Packet Transform (WPT). A “wrapper” feature selection procedure is used to reduce the size of the feature set without sacrificing the classification accuracy. The performance of a Radial Basis Function Network (RBFN) is compared with that of a Support Vector Machine (SVM) by simulating and monitoring machine and sensor faults in an industrial fish cutting machine. Initial results show an 85% reduction in feature set size for an RBFN and a 92.5% reduction in feature set size for a SVM.

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