Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding. (1st March 2017)

• Record Type: Journal Article
• Authors: Kang, Shouqiang; Ma, Danyang; Wang, Yujing; Lan, Chaofeng; Chen, Qingguo; Mikulovich, V.I.
• Is Part Of: Mechanical systems and signal processing. Volume 86:Part A(2017)
• Page Start: 40
• Page End: 57
• Publication Date: 2017-03-01
• Subjects: Ensemble empirical mode decomposition -- Multiple-domain features -- Locally linear embedding -- Relative compensation distance -- Performance degradation assessment; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.10.006
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: To effectively assess different fault locations and different degrees of performance degradation of a rolling bearing with a unified assessment index, a novel state assessment method based on the relative compensation distance of multiple-domain features and locally linear embedding is proposed. First, for a single-sample signal, time-domain and frequency-domain indexes can be calculated for the original vibration signal and each sensitive intrinsic mode function obtained by improved ensemble empirical mode decomposition, and the singular values of the sensitive intrinsic mode function matrix can be extracted by singular value decomposition to construct a high-dimensional hybrid-domain feature vector. Second, a feature matrix can be constructed by arranging each feature vector of multiple samples, the dimensions of each row vector of the feature matrix can be reduced by the locally linear embedding algorithm, and the compensation distance of each fault state of the rolling bearing can be calculated using the support vector machine. Finally, the relative distance between different fault locations and different degrees of performance degradation and the normal-state optimal classification surface can be compensated, and on the basis of the proposed relative compensation distance, the assessment model can be constructed and an assessment curve drawn. Experimental results show that the proposed method can effectively assess different fault locations and different degrees of performance degradation of the rolling bearing under certain conditions. Highlights: A novel method is proposed for rolling bearing performance degradation assessment. Sensitive IMFs of improved EEMD are combined with SVD to extract time-frequency features. Multi-domain feature vectors were constructed to characterize fault information more completely. LLE effectively eliminates correlation and redundancy of vibration signal features. As an assessment index the proposed relative compensation distance is more effective.