Application of adaptive convolutional neural network in rotating machinery fault diagnosis

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (16) : 275-282.

LI Tao1, DUAN Lixiang1, ZHANG Dongning2, ZHAO Shangxin2, HUANG Hui3, BI Caixia3, YUAN Zhuang1

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Abstract

Aiming at solving the problems of fault diagnosis based on machine learning model, such as relying on manual feature extraction quality, dimension disaster, lack of selfadaptation of convolutional neural network (CNN) model construction,a fault diagnosis method of adaptive CNN based on particle swarm optimization (PSO) was proposed and applied to fault diagnosis of rotating machinery. Firstly, onedimensional timedomain signal was transformed into twodimensional timefrequency image; Then, the PSO algorithm was employed to optimize the seven key parameters in the CNN model to construct a deep learning model. Finally, the twodimensional timefrequency image was input into the optimized model to diagnose rotating machinery faults. The results show that the proposed method has high accuracy, stability and adaptability.

Key words

rotating machinery

/ fault diagnosis / convolutional neural network(CNN) / deep learning model / particle swarm optimization(PSO) algorithm

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LI Tao1, DUAN Lixiang1, ZHANG Dongning2, ZHAO Shangxin2, HUANG Hui3, BI Caixia3, YUAN Zhuang1. Application of adaptive convolutional neural network in rotating machinery fault diagnosis[J]. Journal of Vibration and Shock, 2020, 39(16): 275-282

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