A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
http://link.springer.com/content/pdf/10.1007/s10489-020-02011-9.pdf
Reference31 articles.
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2. Sobie C., Freitas C., Nicolai M. (2018) Simulation-driven machine learning: Bearing fault classification. Mechanical Systems and Signal Processing 99:403. https://doi.org/10.1016/j.ymssp.2017.06.025
3. Kumar A., Gandhi C. P., Zhou Y., Kumar R., Xiang J. (2020) Latest developments in gear defect diagnosis and prognosis: A review. Measurement 158:107735
4. Yang Y., Zheng H., Li Y., Xu M., Chen Y. (2019) A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network. ISA transactions 91:235–252
5. Jiao J., Zhao M., Lin J., Zhao J. (2018) A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes. Knowledge-Based Systems 160:237
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