Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model
Joint Authors
Chen, Xutao
Dun, Bosen
Wang, Bei
Yan, Dawen
Zhu, Hong
Wang, Fengtao
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Reliability assessment is a critical consideration in equipment engineering project.
Successful reliability assessment, which is dependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows for the proactive maintenance of equipment.
In this paper, a novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings.
A high relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency domain features over the bearing’s life cycle data.
The kernel principal components which can accurately reflect the performance degradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability.
An example was conducted to validate the proposed method.
The differences in manufacturing, installation, and working conditions of the same type of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and stability of the proposed method.
American Psychological Association (APA)
Wang, Fengtao& Chen, Xutao& Dun, Bosen& Wang, Bei& Yan, Dawen& Zhu, Hong. 2017. Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model. Shock and Vibration،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1204786
Modern Language Association (MLA)
Wang, Fengtao…[et al.]. Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model. Shock and Vibration No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1204786
American Medical Association (AMA)
Wang, Fengtao& Chen, Xutao& Dun, Bosen& Wang, Bei& Yan, Dawen& Zhu, Hong. Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1204786
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204786