Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (7): 193-199.DOI: 10.3778/j.issn.1002-8331.1812-0193

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Research on Bearing Fault Diagnosis of Multi-level Neural Network

ZHONG Lusheng, LIU Dongdong   

  1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
  • Online:2020-04-01 Published:2020-03-28



  1. 华东交通大学 电气与自动化工程学院,南昌 330013


Aiming at the problems of high bearing failure rate, high damage and imperceptibility in industrial production, this paper proposes a multi-level neural network fault diagnosis model that is integrated by improved perceptron, dynamic routing algorithm and stochastic optimization algorithm.Then, the fault features are extracted by the improved multi-layer perceptron, and the dynamic routing algorithm is used to predict and classify the fault features. The classification error is calculated from the loss function. In the error back propagation, the learning rate is filtered by the adaptive learning rate algorithm, and the network model is optimized by the random optimization algorithm(Adam) updating the weight. Finally, the numerical simulation experiment of bearing fault classification is carried out. The results show that the fault diagnosis model can diagnose and classify high-precision bearing faults.

Key words: bearing fault diagnosis, percetron;dynamic routing, neural networks



关键词: 轴承故障诊断, 感知器, 动态路由, 神经网络