Diagnosis of Wear-Induced Breakdown of Machine by Spectrometric Analysis Based on Artificial Neural Network
基于神经网络的机械磨损故障光谱定位诊断法

The spectrometric method to diagnose wear of frictional parts based on artificial neural network (ANN) was established on the basis of analyzing commonly used spectrometric localization diagnosis methods. Thus the training samples were established using the elemental composition of the frictional pair materials as the inputs of ANN and the corresponding frictional parts as the outputs of ANN. The diagnosis to the wear failure locations was realized by coordinating the training samples and training the ANN and making use of the powerful non-linear mapping ability and error-tolerating ability of the ANN. The precision and feasibility of the established diagnosis method were validated by analysis of some examples. It was found that the established diagnosis method was applicable to diagnose the wear status of frictional parts with convenience and good precision.