A rolling bearing fault detection method based on compressed sensing and a neural network

Lu Lu; Jiyou Fei; Ling Yu; Yu Yuan; Lu Lu; Jiyou Fei; Ling Yu; Yu Yuan

doi:10.3934/mbe.2020313

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 5864-5882. doi: 10.3934/mbe.2020313

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A rolling bearing fault detection method based on compressed sensing and a neural network

Lu Lu ¹,
Jiyou Fei ²,
Ling Yu ³,
Yu Yuan ^{2,4
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1.
School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China
2.
College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
3.
China Household Electric Appliance Research Institute, Beijing 100037, China
4.
Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China

Received: 17 June 2020 Accepted: 20 August 2020 Published: 03 September 2020

The high sampling frequency of traditional Nyquist sampling theory not only puts greater requirements on the sampling equipment, but also generates a large amount of data, which increases the difficulty of information transmission and storage. To this end, this paper proposes a rolling bearing fault signal detection method based on compressed sensing combined with a neural network. Based on the theory of compressed sensing, the observations obtained from compression sampling are divided into two sets of data. Given the one set of data, the predictive ability of the nonlinear time series through the neural network can predict the second set of observed values. The predicted observations are used to reconstruct the signal, thereby reducing the amount of data to be stored and transmitted and realizing secondary compression of the signal.
- compressed sensing,
- neural network,
- fault detection,
- feature extraction
Citation: Lu Lu, Jiyou Fei, Ling Yu, Yu Yuan. A rolling bearing fault detection method based on compressed sensing and a neural network[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5864-5882. doi: 10.3934/mbe.2020313

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Abstract

The high sampling frequency of traditional Nyquist sampling theory not only puts greater requirements on the sampling equipment, but also generates a large amount of data, which increases the difficulty of information transmission and storage. To this end, this paper proposes a rolling bearing fault signal detection method based on compressed sensing combined with a neural network. Based on the theory of compressed sensing, the observations obtained from compression sampling are divided into two sets of data. Given the one set of data, the predictive ability of the nonlinear time series through the neural network can predict the second set of observed values. The predicted observations are used to reconstruct the signal, thereby reducing the amount of data to be stored and transmitted and realizing secondary compression of the signal.

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