Original scientific paper

Mechanical bearing fault detection based on two-stage neural network

X. Y. Fu ; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
J. H. Zhao ; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
Z. J. Chen ; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China

Abstract

Bearing is one of the key components widely used in mechanical equipment. Due to overload, fatigue, wear, corrosion and other reasons, bearings are easily damaged during machine operation. Therefore, the monitoring and analysis of the bearing state is very important. It can find the early weak fault of the bearing and prevent the loss caused by the fault. This paper proposes a long-term and short-term network combining the lightweight convolutional block attention module (CBAM-LSTM). In the field of bearing fault detection, the experimental results show that the CBAM-LSTM method can accurately identify a variety of mechanical bearing faults with an accuracy of 99,13 7 %.

Keywords

bearing fault detection; rotating vibration; neural network; CBAM-LSTM

Hrčak ID:

307428

URI

https://hrcak.srce.hr/307428

Publication date:

1.1.2024.

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