可解释性
降级(电信)
断层(地质)
非线性系统
故障检测与隔离
计算机科学
控制理论(社会学)
机器学习
人工智能
模式识别(心理学)
可靠性工程
工程类
物理
地质学
电信
控制(管理)
量子力学
地震学
执行机构
作者
Xiaochuan Li,Shengbing Zhen,Lanlin Yu,Zhe Yang,Chuan Li,David Mba
标识
DOI:10.1016/j.ymssp.2024.111256
摘要
Optimization-based weight learning approaches that incorporate multiple sensor signals or vibration spectral components have demonstrated their effectiveness in monitoring mechanical systems. However, the current weight learning methods are limited by their reliance on linear fusion functions. The exploration of nonlinear link functions, particularly in the context of fusing spectral components in vibration spectra for health indices (HIs) construction, remains largely unexplored. Additionally, the development of formulas for interpreting HIs constructed using nonlinear fusion functions poses significant theoretical challenges. To address these limitations, this study proposes a novel nonlinear optimization-based weight learning model for constructing an HI that captures the nonlinear relationships among spectral components. This HI is designed to pinpoint fault characteristic frequencies while also highlighting other spectral lines associated with bearing degradation modelling. Furthermore, we derive formulas that enable the calculation of the contributions of frequency components to nonlinearly constructed HIs, thereby enhancing the interpretability of the model. By selecting an appropriate kernel function, subtle changes resulting from incipient faults and the underlying degradation process can be effectively captured. The effectiveness of the proposed model in detecting incipient faults, modelling degradation, and identifying fault types is validated through two run-to-failure case studies and a gearbox bearing experiment.
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