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Weight-Based Large Margin Hyperdisks for Explainable Performance Degradation Modeling
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AI
DOI：10.1109/TIM.2024.3412223.png)
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摘要
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Machine performance degradation modeling aims to construct a reliable health indicator (HI) for machine health evaluation. Deep learning-based HIs are not reliable due to their inexplicability and strong volatility in assessing machine degradation. Recently, it has been an emerging topic for machine fault diagnosis based on hyperdisk modeling. Nevertheless, hyperdisk-based performance degradation modeling for HI construction needs further exploration. In this article, weight-based large margin hyperdisks for explainable machine degradation modeling are proposed to construct an explicit HI. The data preprocessing of the proposed methodology only requires simple transforms including Hilbert transform (HT) and Fourier transform. Then, based on squared envelope spectra (SES), two hyperdisks are respectively developed to fully characterize spectral distributions under different machine health conditions. Further, a weight-based large margin hyperdisk optimization model is established for explainable machine degradation modeling, where an explicit and reliable HI can be well developed by fusing life cycle envelope spectra and optimized weight coefficients. Optimized weight coefficients that can reveal underlying informative frequencies provide an insightful perspective for the interpretability of machine performance degradation and its relevant HI. Two bearing run-to-failure datasets are studied to show the effectiveness of the proposed model and its advantages over other existing methods.
Keyword:
Feature extraction
Degradation
Reliability
Fault diagnosis
Market research
Data models
Vibrations
Bearing
explicit health indicator (HI)
hyperdisk
optimized weight coefficients
performance degradation modeling
squared envelope spectra (SES)
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
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