An intelligent fault diagnosis method for bearings with variable rotating speed based on pythagorean spatial pyramid pooling CNN
Open accessAbstract
Deep learning methods have been introduced for fault diagnosis of rotating machinery. Most methods have good performance when processing bearing data at a certain rotating speed. However, most rotating machinery in industrial practice has variable working speed. When processing the bearing data with variable rotating speed, the existing methods have low accuracies, or need complex parameter adjustments. To solve this problem, a fault diagnosis method based on continuous wavelet transform scalogram (CWTS) and Pythagorean spatial pyramid pooling convolutional neural network (PSPP-CNN) is proposed in this paper. In this method, continuous wavelet transform is used to decompose vibration signals into CWTSs with different scale ranges according to the rotating speed. By adding a PSPP layer, CNN can process CWTSs in different sizes. Then the fault diagnosis of variable rotating speed bearing can be carried out by a single CNN model without complex parameter adjustment. Compared with a spatial pyramid pooling (SPP) layer that has been used in CNN, a PSPP layer locates as front layer of CNN. Thus, the features obtained by PSPP layer can be delivered to convolutional layers for further feature extraction. According to experiment results, this method has higher diagnosis accuracy for variable rotating speed bearing than other methods. In addition, the PSPP-CNN model trained by data at some rotating speeds can be used to diagnose bearing fault at full working speed.
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Signal processing methods are employed to preprocess 1D data to convert to time-frequency domain so CNN can handle 2D time-frequency spectrum.
“First, the signal processing methods, such as the wavelet packet [369][370][371], the continue wavelet transform [372,373], the dual-tree complex wavelet transform [374,375], and the synchrosqueezing transform [376], are employed to preprocess the input 1D data, which are expected to convert the signals from the time domain to the time-frequency domain.”
From Applications of machine learning to machine fault diagnosis: A review and roadmap (2020) · cites this paper as [373]
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Bin, R., Cui, J., Zhi-Xin, J., & Gang, L. (2021) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-nanjing52125.2021.9613008
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A bearing fault diagnosis method using a dynamic model with deep domain adaptation
Zhang, C., Huang, X., Liu, Z., Zhang, J., Lei, T., & Zhong, S. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2803/1/012034
Self-citationMultimodal mechanical equipment fault diagnosis using dual-channel convolutional neural network
Li, W. L. W., Huang, K., Mo, C., & Xu, T. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae4db6
Talssi, F., Jeffali, F., Assia, S., Barkany, A. E., & Bouchnaif, J. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17802-6
Marhrane, R., Jeffali, F., Bouchnaif, J., & Barkany, A. E. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-03011-1
- Authors
- Guo, S., Yang, T., Gao, W., Zhang, C., & Zhang, Y.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Yang, Tao
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-11-09
- Volume
- 18
- Issue
- 11
- Pages
- 3857
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6263722
- OpenAlex
- W2901856126
- Cited by
- 74
- References
- 38
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 3 · 2023: 9 · 2022: 17 · 2021: 14 · 2020: 10 · 2019: 11
- FWCI
- 5.42
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Program of International Science and Technology Cooperation of China (2014DFA60990)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Mechanical Engineering › Gear and Bearing Dynamics Analysis
- Keywords
- bearing fault diagnosis, spatial pyramid pooling, vibration signals, rotating machinery, convolutional neural networks, feature extraction
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