RESEARCH PAPER
Rolling bearing fault diagnosis method based on multi-scale pooling residual convolutional neural network under noisy environment
1
Lanzhou university of technology, China
2
Yunnan Wenshan Aluminum Co, Ltd, China
Submission date: 2024-05-03
Final revision date: 2024-06-15
Acceptance date: 2024-08-08
Online publication date: 2024-08-11
Publication date: 2024-08-11
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):192167
HIGHLIGHTS
- Wide convolution kernel is used for feature extraction.
- The MSPFE module combined with UPA is proposed.
- Gated convolution and a new activation function IReLU are put forward. On the basis of the ReLU function, the IReLU activation function introduces a new continuous function in the negative half axis to overcome the shortcomings of the existing activation function.
KEYWORDS
TOPICS
ABSTRACT
To address the issues of unstable performance and poor generalization ability of bearing fault diagnosis model caused by strong noise and variable operating conditions in actual production，this paper proposes a rolling bearing fault diagnosis method based on MSPRCNN model. By converting vibration signals to frequency domain with FTand utilizing wide convolution kernels for feature extraction, the approach aims to enhance fault detection. A MSPFE module captures information at different scales to simplify complexity, while an UPA module establishes correlations between frequency domain positions. To reduce the impact of noise and address the vanishing gradient problem, the MSPRCNN model employs GC instead of standard convolution, and utilizes the IReLU activation function to improve model feature representation. Experimental results on two datasets show that the fault recognition accuracy is 98.71% under variable loads and 98.2% under variable speeds. The MSPRCNN model outperforms other methods in fault recognition and generalization in noisy environments.
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CITATIONS (1):
1.
Rolling bearing fault diagnosis method based on MTF-MMCNN
Ruicheng Feng, Qiyue Zhang, Lu Wang, Manwen Li, Chunli Lei
Nondestructive Testing and Evaluation
Ruicheng Feng, Qiyue Zhang, Lu Wang, Manwen Li, Chunli Lei
Nondestructive Testing and Evaluation
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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