Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis applications

In this paper, results are presented from a comprehensive series of studies aimed at assessing the suitability of multilayered perceptron (MLP) and radial basis function (RBF) networks for use in embedded, microcontroller-based, condition monitoring and fault diagnosis (CMFD) applications. Our assessment criteria include the performance of each classifier on a range of CMFD-related problems, such as situations where there may be multiple faults present simultaneously, or where 'unknown' faults may occur. In addition, the processor and memory requirements of each classifier are compared and discussed. On the basis of the results obtained in these studies, it is argued that each form of classifier has both strengths and weaknesses, and that neither is suitable for use in all CMFD applications. The paper concludes by demonstrating that, where memory and processor limits allow, the best performance may be obtained through use of a fusion classifier containing both MLP and RBF components.