NDLI: Robust bearing degradation assessment method based on improved CVA

Content Provider	IET Digital Library
Author	Wang, Baoxiang Pan, Hongxia Yang, Wei
Abstract	Owing to the importance of rolling bearings in mechanical systems, many meaningful works have been presented for bearing degradation assessment. However, most of contributions fail to take into account the influence of input features on the performance of a degradation model. As a potential multivariable process control method considering serial correlations, canonical variate analysis (CVA) is introduced to detect abnormal behaviours of rolling bearings. This study combines the CVA and support vector data description (SVDD) to develop a robust approach to weaken or remove the negative impact of input data on bearing degradation estimation. First, the time-domain features extracted from healthy vibration signals are fused by CVA to generate projection data for SVDD training. Second, the distance of new variables to the centre of the hypersphere is calculated as an indicator to describe the degradation of rolling bearings. To demonstrate the effectiveness and robustness of the proposed method, two different experiments on bearing run-to-failure tests are conducted. Experimental results validate that the proposed work is capable of detecting bearing failure under various working conditions. Besides, comparison of the proposed work with the original CVA is implemented. The robustness of the proposed work is enhanced obviously compared with the original CVA.
Starting Page	637
Ending Page	645
Page Count	9
ISSN	17518822
Volume Number	11
e-ISSN	17518830
Issue Number	Issue 5, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/11/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2016.0391
Journal	IET Science, Measurement & Technology
Publisher Date	2017-03-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bearing Run-to-failure Testing Canonical Variate Analysis Civil And Mechanical Engineering Computing Data Description Digital Signal Processing Failure (mechanical) Feature Extraction Healthy Vibration Signal Fusion Improved CVA Knowledge Engineering Technique Learning in AI Mathematical Analysis Mechanical Component Mechanical Engineering Mechanical Engineering Application of IT Mechanical Engineering Computing Mechanical System Mechanical Testing Potential Multivariable Process Control Method Robust Bearing Degradation Assessment Method Rolling Bearings Serial Correlation Signal Processing Signal Processing And Detection Support Vector Data Description SVDD Testing Time Domain Analysis Time-domain Feature Extraction Training Model Vibrations Vibrations And Shock Wave
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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