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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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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
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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
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
Self-citationHighly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2864759 · Cited by 1,690
Self-citationMachine learning and data mining in manufacturing
Doğan, A., & Birant, D. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114060 · Cited by 750
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis
Shao, S., Yan, R., Lu, Y., Wang, P. E., & Gao, R. X. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2925247 · Cited by 445
Self-citationReview of tool condition monitoring in machining and opportunities for deep learning
Serin, G., Şener, B., Özbayoğlu, A. M., & Ünver, H. Ö. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05449-w · Cited by 382
Saufi, M. S. R. M., Ahmad, Z. A., Leong, M. S., & Lim, M. H. (2019) · IEEE Access · doi:10.1109/access.2019.2938227 · Cited by 323
Gawde, S., Patil, S., Kumar, S., Kamat, P. V., Kotecha, K. V., & Abraham, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106139 · Cited by 327
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
Serradilla, O., Zugasti, E., Rodriguez, J., & Zurutuza, U. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03004-y · Cited by 377
A Review on Deep Learning Applications in Prognostics and Health Management
Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019) · IEEE Access · doi:10.1109/access.2019.2950985 · Cited by 257
Morariu, C., Morariu, O., Răileanu, S., & Borangiu, T. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103244 · Cited by 248
AlShorman, O., Irfan, M., Saad, N., Zhen, D., Haider, N., Glowacz, A., & AlShorman, A. (2020) · Shock and Vibration · doi:10.1155/2020/8843759 · Cited by 215
Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review
Meng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018) · Sustainability · doi:10.3390/su10124779 · Cited by 190
Wang, H., Shi, L., Song, L., Cui, L., & Wang, P. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2928346 · Cited by 191
Self-citationMechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion
Huang, M., Liu, Z., & Tao, Y. (2019) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2019.101981 · Cited by 168
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Huang, Y., Chen, C.-H., & Huang, C.-J. (2019) · IEEE Access · doi:10.1109/access.2019.2940769 · Cited by 144
Gawde, S., Patil, S., Kumar, S., & Kotecha, K. V. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10243-z · Cited by 123
Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108818 · Cited by 122
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Gai, J., Zhong, K., Du, X., Yan, K., & Shen, J. (2021) · Measurement · doi:10.1016/j.measurement.2021.110079 · Cited by 95
A roadmap to fault diagnosis of industrial machines via machine learning: A brief review
Vashishtha, G., Chauhan, S., Sehri, M., Zimroz, R., Dumond, P., Kumar, R., & Gupta, M. K. (2024) · Measurement · doi:10.1016/j.measurement.2024.116216 · Cited by 100
Li, Z., Wang, Y., & Wang, K. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.07.002 · Cited by 82
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Hasan, M. J., Sohaib, M., & Kim, J.-M. (2021) · Sensors · doi:10.3390/s21124070 · Cited by 78
Condition-Based Monitoring and Maintenance: State of the Art Review
Ali, A., & Abdelhadi, A. (2022) · Applied Sciences · doi:10.3390/app12020688 · Cited by 74
Zhang, Z., Qian, K., Schuller, B. W., & Wollherr, D. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2997094 · Cited by 71
Kumar, P., Khalid, S., & Kim, H. S. (2023) · Mathematics · doi:10.3390/math11133008 · Cited by 60
Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN
Pan, J., Qu, L., & Peng, K. (2021) · Entropy · doi:10.3390/e23060751 · Cited by 57
A deep learning approach for electric motor fault diagnosis based on modified InceptionV3
Xu, L., Teoh, S. S., & Ibrahim, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-63086-9 · Cited by 70
Raouf, I., Kumar, P., & Kim, H. S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125137 · Cited by 64
Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders
Zhang, S., Sun, Z., Long, J., Li, C., & Bai, Y. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.12.004 · Cited by 57
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Sai, S. A., Venkatesh, S. N., Dhanasekaran, S., Balaji, P. A., Sugumaran, V., Lakshmaiya, N., & Paramasivam, P. (2023) · Machines · doi:10.3390/machines11080778 · Cited by 50
Mueller, P. N., Woelfl, L., & Can, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106834 · Cited by 54
Wang, M., Zhang, Z., Li, K., Zhang, Z., Sheng, Y., & Liu, S. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04289-7 · Cited by 49
Sheik, A. G., Kumar, A., Patnaik, R., Kumari, S., & Bux, F. (2024) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2023.2252313 · Cited by 47
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Kim, G., Choi, J. G., Ku, M., Cho, H., & Lim, S. (2021) · IEEE Access · doi:10.1109/access.2021.3115665 · Cited by 44
Kim, D., Lim, D., & Park, J. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3129630 · Cited by 43
A Review on Data-Driven Condition Monitoring of Industrial Equipment
Qi, R., Zhang, J., & Spencer, K. (2022) · Algorithms · doi:10.3390/a16010009 · Cited by 49
Li, Y., Cheng, G., Pang, Y., & Kuai, M. (2018) · Sensors · doi:10.3390/s18061735 · Cited by 42
Gonzalez-Jimenez, D., del Olmo, J., Poza, J., Garramiola, F., & Sarasola, I. (2021) · Energies · doi:10.3390/en14164886 · Cited by 42
Zhao, F., Jiang, Y., Cheng, C., & Wang, S. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0691 · Cited by 41
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Liao, Y., Huang, R., Li, J., Chen, Z., & Li, W. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00566-3 · Cited by 40
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Hasan, M. J., Islam, M. M. M., & Kim, J.-M. (2021) · Sensors · doi:10.3390/s22010056 · Cited by 43
Kim, E. K., Lee, H., Kim, J. Y., & Kim, S. (2020) · Applied Sciences · doi:10.3390/app10113755 · Cited by 45
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Pan, H., Sun, W., Sun, Q., & Gao, H. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00568-1 · Cited by 43
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Da, T. N., Thanh, P. N., & Cho, M.-Y. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101164 · Cited by 36
Cunha, R. G. C., Junior, E. T. D. S., & Medeiros, C. M. D. S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103461 · Cited by 39
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Hasan, M. J., Sohaib, M., & Kim, J.-M. (2020) · Sensors · doi:10.3390/s20247205 · Cited by 33
Ayman, A., Onsy, A., Attallah, O., Brooks, H., & Morsi, I. (2024) · Measurement · doi:10.1016/j.measurement.2024.116589 · Cited by 36
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Lee, D., Jeong, J.-H., Yoon, S. H., & Chae, Y. T. (2019) · Energies · doi:10.3390/en12173247 · Cited by 25
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Xie, Y., Yang, G., Chen, H., Zhao, Z., & Leng, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522623 · Cited by 16
Chen, L., Zhang, Z., & Cao, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228324 · Cited by 14
Zhang, H., Zhang, Q., Shao, S., Niu, T., Yang, X., & Ding, H. (2020) · Shock and Vibration · doi:10.1155/2020/8888627 · Cited by 15
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Han, H., Jiang, B., Xiang, D., Shi, Y., Liu, S., Shang, C., Zhao, X., Zhang, H., & Sun, Y. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2023.111526 · Cited by 9
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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