关键词: 离心泵, 故障诊断, 位错叠加法, 概率神经网络, 哈里斯鹰优化算法

Abstract:  Based on dislocation superposition method and improved probabilistic neural network， a fault diagnosis method of centrifugal pumps was proposed to solve the problems of online fault diagnosis using acoustic radiation signals of centrifugal pumps under strong background noise. Firstly， the acoustic radiation signals of centrifugal pumps were denoised by dislocation superposition method to enhance the fault information in acoustic radiation signals and improve the signal-to-noise ratio. The time domain features of acoustic signals were extracted to construct the time domain feature matrix. After dimensionality reduction of the obtained time domain feature matrix through principal component analysis， which was used as the inputs of machine learning probabilistic neural network. At the same time， Harris hawk optimization algorithm was used to optimize the parameters of the probabilistic neural network to get the diagnosis model， and then the improved probabilistic neural network was used to recognize the patterns of the centrifugal pump faults， and compared with a variety of diagnostic methods. The experimental results show that the dislocation superposition method may highlight the signal characteristics and realize signal enhancement， and the improved probabilistic neural network has a good ability of online fault diagnosis of centrifugal pump acoustic radiation signals. 

Key words: centrifugal pump, fault diagnosis, dislocation superposition method, probabilistic neural network, Harris hawk optimization algorithm