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A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network
Open accessAbstract
Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.
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“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 [372]
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Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
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Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine
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Metadata
- Authors
- Guo, S., Yang, T., Gao, W., & Zhang, C.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Yang, Tao
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-05-04
- Volume
- 18
- Issue
- 5
- Pages
- 1429
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5982639
- OpenAlex
- W2801457104
- Cited by
- 233
- References
- 29
- Citations by year
- 2026: 16 · 2025: 17 · 2024: 20 · 2023: 34 · 2022: 30 · 2021: 45 · 2020: 36 · 2019: 30 · 2018: 5
- FWCI
- 17.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Chinese National Science and Technology Support Program (2015BAA06B02)
- 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 › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Keywords
- fault diagnosis, rotating machinery, convolutional neural networks, vibration signals, rotor fault diagnosis, time-frequency analysis, feature extraction
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