Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm

Ran Li; Yang Liu

doi:10.1109/SAFEPROCESS45799.2019.9213345

Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm

Ran Li, Yang Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

A hybrid dimension reduction algorithm based on feature selection and kernel principal component analysis (KPCA) is proposed in this paper to better realize the classification of the planetary gearbox faults. Firstly, in order to reduce the redundancy of some unnecessary features in the sample to a greater extent and the complexity of the kernel matrix calculation, a multi-criterion feature selection method is used to eliminate the irrelevant features. Secondly, through KPCA, the nonlinear principal component of the selected features is built. Then, fault is recognized by put the feature subset into the SVM classification. The proposed algorithm is applied to a planetary gearbox fault diagnosis experiment, and the experimental results show that the proposed algorithm outperforms the ones which employ feature selection or KPCA separately.

Original language	English
Title of host publication	Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes
Subtitle of host publication	SAFEPROCESS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	23-28
Number of pages	6
ISBN (Electronic)	9781728106816
ISBN (Print)	978128106823
DOIs	https://doi.org/10.1109/SAFEPROCESS45799.2019.9213345
Publication status	Published - 6 Oct 2020
Externally published	Yes
Event	11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes - Xiamen, China Duration: 5 Jul 2019 → 7 Jul 2019 Conference number: 11 https://search.worldcat.org/title/proceedings-of-2019-11th-caa-symposium-on-fault-detection-supervision-and-safety-for-technical-processes-caa-safeprocess-2019-xiamen-china-july-05-07-2019/oclc/1224926061

Conference

Conference	11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes
Abbreviated title	CAA SAFEPROCESS 2019
Country/Territory	China
City	Xiamen
Period	5/07/19 → 7/07/19
Internet address	https://search.worldcat.org/title/proceedings-of-2019-11th-caa-symposium-on-fault-detection-supervision-and-safety-for-technical-processes-caa-safeprocess-2019-xiamen-china-july-05-07-2019/oclc/1224926061

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Cite this

Li, R., & Liu, Y. (2020). Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm. In Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes: SAFEPROCESS 2019 (pp. 23-28). [9213345] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAFEPROCESS45799.2019.9213345

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title = "Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm",

abstract = "A hybrid dimension reduction algorithm based on feature selection and kernel principal component analysis (KPCA) is proposed in this paper to better realize the classification of the planetary gearbox faults. Firstly, in order to reduce the redundancy of some unnecessary features in the sample to a greater extent and the complexity of the kernel matrix calculation, a multi-criterion feature selection method is used to eliminate the irrelevant features. Secondly, through KPCA, the nonlinear principal component of the selected features is built. Then, fault is recognized by put the feature subset into the SVM classification. The proposed algorithm is applied to a planetary gearbox fault diagnosis experiment, and the experimental results show that the proposed algorithm outperforms the ones which employ feature selection or KPCA separately.",

keywords = "Dimension reduction, Feature selection, Kernel principal component analysis, Planetary gearbox",

author = "Ran Li and Yang Liu",

note = "Funding Information: This work was supported by National Natural Science Foundation of China (Grant No. 61703244), China Postdoctoral Science Foundation (Grant No. 20l8TlI070l), and Special Postdoc Creative Funding of Shandong Province of China (Grant No. 201702036). Publisher Copyright: {\textcopyright} 2019 IEEE.; 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, CAA SAFEPROCESS 2019 ; Conference date: 05-07-2019 Through 07-07-2019",

year = "2020",

month = oct,

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doi = "10.1109/SAFEPROCESS45799.2019.9213345",

language = "English",

isbn = "978128106823",

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booktitle = "Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

url = "https://search.worldcat.org/title/proceedings-of-2019-11th-caa-symposium-on-fault-detection-supervision-and-safety-for-technical-processes-caa-safeprocess-2019-xiamen-china-july-05-07-2019/oclc/1224926061",

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Li, R & Liu, Y 2020, Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm. in Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes: SAFEPROCESS 2019., 9213345, Institute of Electrical and Electronics Engineers Inc., pp. 23-28, 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, Xiamen, China, 5/07/19. https://doi.org/10.1109/SAFEPROCESS45799.2019.9213345

Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm. / Li, Ran; Liu, Yang.

Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes: SAFEPROCESS 2019. Institute of Electrical and Electronics Engineers Inc., 2020. p. 23-28 9213345.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Liu, Yang

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N2 - A hybrid dimension reduction algorithm based on feature selection and kernel principal component analysis (KPCA) is proposed in this paper to better realize the classification of the planetary gearbox faults. Firstly, in order to reduce the redundancy of some unnecessary features in the sample to a greater extent and the complexity of the kernel matrix calculation, a multi-criterion feature selection method is used to eliminate the irrelevant features. Secondly, through KPCA, the nonlinear principal component of the selected features is built. Then, fault is recognized by put the feature subset into the SVM classification. The proposed algorithm is applied to a planetary gearbox fault diagnosis experiment, and the experimental results show that the proposed algorithm outperforms the ones which employ feature selection or KPCA separately.

AB - A hybrid dimension reduction algorithm based on feature selection and kernel principal component analysis (KPCA) is proposed in this paper to better realize the classification of the planetary gearbox faults. Firstly, in order to reduce the redundancy of some unnecessary features in the sample to a greater extent and the complexity of the kernel matrix calculation, a multi-criterion feature selection method is used to eliminate the irrelevant features. Secondly, through KPCA, the nonlinear principal component of the selected features is built. Then, fault is recognized by put the feature subset into the SVM classification. The proposed algorithm is applied to a planetary gearbox fault diagnosis experiment, and the experimental results show that the proposed algorithm outperforms the ones which employ feature selection or KPCA separately.

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Fault Diagnosis for the Planetary Gearbox Based on a Hybrid Dimension Reduction Algorithm

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