Fault diagnosis of pumping machinery using artificial neural networks

Pumping costs within British industry are enormous, with the potential for considerable financial savings through fault diagnosis and condition-based maintenance. Accurate condition monitoring data interpretation is a key requirement in pump fault diagnosis. However, the human skills required to transform monitored data into maintenance information are often unavailable. Artificial neural networks (ANNs) are proposed for automation of this skill in the development of a pumping system decision support tool, the key requirement of which is accurate pump fault diagnosis. The cumulative sum charting procedure was used to establish a knowledge base of fault data for ANN implementation based on historical parameter measurements. Various preprocessing techniques were investigated in relation to generalization ability and convergence rates during the learning phase. Preprocessing predominantly affected ANN convergence rate, with the quality of training data crucial to generalization ability. ANNs could provide accurate, incipient fault diagnosis of pumping machinery based on real industrial data corresponding to historical pump faults.