Convolutional neural network-based hidden Markov models for rolling element bearing fault identification

作者：

Highlights：

• CNN-based HMMs are proposed to detect faults in rolling element bearings.

• This model takes advantage of both the CNN and HMMs for their strong ability in data feature learning and pattern recognition.

• The agreeable classification accuracy and stability are tested by benchmark data and experimental data investigations.

• The average classification accuracy ratios are 98.125% and 98% for two data series, which is better than those of CNN model alone, SVM and BP neural network.

摘要

• CNN-based HMMs are proposed to detect faults in rolling element bearings.• This model takes advantage of both the CNN and HMMs for their strong ability in data feature learning and pattern recognition.• The agreeable classification accuracy and stability are tested by benchmark data and experimental data investigations.• The average classification accuracy ratios are 98.125% and 98% for two data series, which is better than those of CNN model alone, SVM and BP neural network.

论文关键词：Convolutional neural network,HMM,Feature extraction,Rolling element bearing,Fault diagnosis

论文评审过程：Received 22 June 2017, Revised 19 December 2017, Accepted 25 December 2017, Available online 26 December 2017, Version of Record 14 February 2018.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.12.027