A new bearing fault diagnosis method based on improved weighted multi-scale morphological filter and multi-headed self-attention capsule restricted boltzmann network
Affiliations: [a]
School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, Liaoning, China
| [b]
School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, Liaoning, China
| [c]
School of Mechanical Engineering, Dalian Jiaotong University, Dalian, Liaoning, China
Correspondence:
[*]
Corresponding author. Xudong Song, School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China. E-mail: [email protected].
Abstract: Intelligent bearing fault diagnosis plays an important role in improving equipment safety and reducing equipment maintenance costs. Noise in the signal can seriously reduce the accuracy of fault diagnosis. To improve the accuracy of fault diagnosis, a novel noise reduction method based on weighted multi-scale morphological filter (WMMF) is proposed. Firstly, Teager energy operator (TEO) is used to amplify the morphological information of the signal. Then, a scale filtering operator using envelope entropy (SFOEE) is proposed to select appropriate scales. At these scales, the noise in the signal can be adequately suppressed. A new weighting method is proposed to integrate the selected scales to construct the WMMF. Finally, multi-headed self-attention capsule restricted boltzmann network (MSCRBN) is proposed to diagnose bearing faults.The performance of the TEO-SFOEE-WMMF-MSCRBN fault diagnosis method is verified on the CWRU dataset. Compared with existing fault diagnosis methods, this approach achieves 100% identification accuracy. The experimental results indicate that the proposed diagnosis method can effectively resist noise and precisely diagnose bearing faults.
