Dynamic Evaluation of Degradation Space Variation for Machine Condition Monitoring

Zhang, Guangyao; Wang, Yi; Li, Xiaomeng; Qin, Yi; Tang, Baoping

doi:10.1109/tim.2022.3191716

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…A new fusion model combining a degradation-constraint loss term and variational autoencoder was proposed by Qin et al for generating various HIs [34]. In addition, other multi-feature fusion strategies have been developed for condition monitoring of rotating machines [35][36][37][38]. However, the interpretability of most HIs based on multifeature fusion is poor, and the construction of some fusion HIs relies on full lifecycle data.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear characterization of enhanced and generalized Hjorth’s feature space for bearing condition monitoring

Li,

Wang,

et al. 2023

Meas. Sci. Technol.

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The degradation assessment of rolling bearings provides a reasonable maintenance plan for the safe operation of mechanical equipment. The general strategy for bearing condition monitoring is to construct a health indicator (HI) to characterize different degradation stages. A preferable HI that can sensitively detect initial faults and track machine degradation is crucial to developing timely maintenance strategies for mechanical equipment to avoid catastrophic accidents. However, many developed and reported HIs are still insensitive to early faults, resulting in delayed maintenance schedules. To identify the incipient defects as early as possible, a novel HI constructed by nonlinear characterization of enhanced and generalized Hjorth’s feature space based on extended probability entropy is proposed in this paper. Firstly, the time-frequency spectral amplitude modulation helps to enhance the characteristics of the original signal with the amplitude editing in the time-frequency domain. Then, three new features of generalized Hjorth’s parameter combinations are designed and combined with other similar feature combinations to construct a high-dimensional enhanced and generalized Hjorth’s feature space. On this basis, a set of low-dimensional sensitive features is obtained by nonlinearly characterizing high-dimensional features through extended probability entropy after these features are standardized. Finally, a novel HI is developed by calculating the distance between the minimum volume ellipse (MVE) center of the low-dimensional feature subspace based on nonlinear characterization and the low-dimensional feature vector of the real-time monitoring signal. The performance of the proposed approach is verified in three cases, whose experimental results indicate that the proposed HI is more sensitive and significant in detecting early faults compared to some current HIs.

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