Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation. (February 2020)

• Record Type: Journal Article
• Authors: Chen, Fafa; Yang, Yunpeng; Tang, Baoping; Chen, Baojia; Xiao, Wenrong; Zhong, Xianyou
• Is Part Of: Measurement. Volume 151(2020)
• Publication Date: 2020-02
• Subjects: Mechanical equipment -- Wavelet packet -- Information entropy -- Operation state -- Performance degradation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107116
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An improved performance degradation prediction method was proposed. Multi-kernel RVM can accurately predict the performance degradation for equipment. Information granulation can obtain the interval range to bracket the prediction result. The prediction ability of this method is prior to poly-kernel RVM and RBF-kernel RVM. Abstract: An operation condition evaluation and prediction method based on wavelet packet information entropy and multi-kernel relevance vector machine is proposed for mechanical equipment in this paper. Firstly, the vibration signals of mechanical equipment are decomposed into high-frequency signals and low-frequency signals with different scales by using the theory of wavelet packet decomposition. The complexity and irregularity of the frequency-varying signals are represented by information entropy. Then, these constructed characteristic indicators are input to the relevance vector machine for performance degradation prediction. Finally, two kinds of roller bearings used in the experiment are taken as an example to verify the effectiveness of the method. The results show that the proposed method for evaluating the operational state of mechanical equipment can fully extract the characteristic information representing the operational state of roller bearings. It provides a new theoretical and practical support for mechanical equipment operational state evaluation and prediction.