Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

Authors

  • Liqun Hou North China Electric Power University
  • Zijing Li North China Electric Power University

DOI:

https://doi.org/10.3991/ijoe.v16i02.11953

Keywords:

fault diagnosis, tunable Q-factor wavelet transform, convolutional neural network, rolling bearing

Abstract


Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.

Author Biographies

Liqun Hou, North China Electric Power University

A/Prof, Department of Automation

Zijing Li, North China Electric Power University

Department of Automation

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Published

2020-02-12

How to Cite

Hou, L., & Li, Z. (2020). Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network. International Journal of Online and Biomedical Engineering (iJOE), 16(02), pp. 47–61. https://doi.org/10.3991/ijoe.v16i02.11953

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Section

Papers