An integrated framework via key-spectrum entropy and statistical properties for bearing dynamic health monitoring and performance degradation assessment

• Published in: Mechanical systems and signal processing 2023-03, Vol.187, p.109955, Article 109955
• Main Authors: Yao, Renhe, Jiang, Hongkai, Yang, Chunxia, Zhu, Hongxuan, Liu, Chaoqiang
• Format: Article
• Language: English
• Subjects: Dynamic health monitoring; Health phase segmentation strategy; Joint statistical alarm strategy; Key-spectrum entropy; Mechanical bearings; Performance degradation assessment
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2022.109955

•Definition of key-spectrum coined MKSFS and a new entropic index coined MKSFS-FE.•Proposal of an integrated framework via MKSFS, MKSFS-FE and statistical properties.•The dynamic fault alarm and identification strategy for online scenarios.•The health state segmentation and pattern analysis strategy for offline scenarios.•Validation of the framework through eighteen sets of bearing degradation signals. Dynamic health monitoring (DHM) and performance degradation assessment (PDA) is critical for mechanical bearings throughout their long in-service life. For this issue, it is currently rare to find a framework with interpretable and automatic approaches developed from pure signal processing techniques and statistical theories. Therefore, an integrated framework via key-spectrum entropy and statistical properties for bearing DHM and PDA is developed in this paper, which integrates the proposed key spectrum, key-spectrum entropy, joint statistical alarm and fault identification strategy, health phase segmentation strategy, and three-dimensional (3D) key spectrums. First, a Kurtosis-Energy metric is defined to extract the key spectrum, which is reconstructed by two wavelet-decomposed sub-bands where the interference components are suppressed. A new health index (HI) of key-spectrum entropy is then defined to quantify the bearing degradation process. Second, a joint statistical alarm and fault identification strategy via updated HIs and key spectrum is proposed to form a DHM methodology for implementing bearing dynamic fault detection and recognition. Third, a health phase segmentation strategy and 3D key spectrums are developed to form a PDA methodology for implementing bearing health phase assessment and degradation pattern analysis. Comprehensive evaluations and comparisons on eighteen sets of bearing degradation vibration signals demonstrate the validity of the proposed framework, as well as its great practical application prospects.