Rolling bearing fault diagnosis based on the enhanced channel attention network

ABSTRACT

This paper proposes a rolling bearing fault diagnosis method based on the enhanced channel attention network. The vibration signals are collected via a wireless sensor node, which are input to the neural network, and channel attention block is used to strengthen the assignment of important features, so that the attention of network is paid onto the critical fault information. Furthermore, channel attention block and residual convolution block are combined to form an enhanced channel attention network to extract the detail features. Experiment results show that the model can achieve 100% recognition accuracy for rolling bearings in various working conditions. It is also shown that the proposed new learning algorithm can provide a higher diagnosis accuracy than those state-of-the-arts in a strong disturbance environment, which reflects better robustness and generalisation ability.

KEYWORDS:

• Fault diagnosis
• rolling bearing
• vibration signal
• residual structure
• mechanism of attention

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the [National Key R&D Funding] under Grant [No. 2018YFB1403702]; [The Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars] under Grant [No. LR22F030003]; [The National Natural Science Foundation of China] under Grant [No. 61873237]; [The Fundamental Research Funds for the Provincial Universities of Zhejiang] under Grant [No. RF-A2019003]; and [Major Project of Science and Technology Innovation in Ningbo City] under Grant [No. 2019B1003].