Robust Diagnosis Method of Equipment Fault Based on Weighted Probabilistic Neural Network*

The fault diagnosis of equipment is important for the operation and maintenance management of industrial process. However, the abnormality of key equipment operation data will affect its fault diagnosis effect. In this paper, we reduce the influence of abnormal process data on fault diagnosis results by an improved probabilistic neural network (PNN). Firstly, the data used to build the network is analyzed to characterize its distribution in feature space, and the pattern layer output is calculated to reduce the effect of outliers. Secondly, in order to obtain a robust center-of-mass representation of the data in the feature space, a centroid that is robust to outliers is designed and used in the weighting strategy of the network. Then, the input weighting strategy of the network is designed and determined by the similarity between the pattern layer centers with high confidence and the network input. Finally, experiments have been conducted in a classification task and a wing damage task, and the results show that the method is effective under data contamination.