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A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing
Open accessAbstract
Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.
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Metadata
- Authors
- Shao, S.-Y., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X.
- Affiliations
- Case Western Reserve University (US)
- Corresponding author
- Yan, Ru-Qiang
- Venue
- Chinese Journal of Mechanical Engineering
- Publisher
- Elsevier BV
- Published
- 2017-10-23
- Volume
- 30
- Issue
- 6
- Pages
- 1347-1356
- ISSN
- 1000-9345 (ISSN-L)
- 2192-8258
- OpenAlex
- W2769634371
- Cited by
- 167
- References
- 52
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 31 · 2023: 23 · 2022: 24 · 2021: 26 · 2020: 22 · 2019: 12 · 2018: 8
- FWCI
- 7.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Chinese Journal of Mechanical Engineering (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (51575102)
- The Fundamental Research Funds for the Central Universities and Research Innovation Program for College Graduates of Jiangsu Province (KYLX16_0191)
- 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
- induction motor fault diagnosis, deep belief network, vibration signals, frequency spectrum features, feature extraction, restricted Boltzmann machine, hierarchical feature learning
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