Stochastic Identification and Analysis of Long-Term Degradation Through Health Index Data

Hamid Shiri () and Pawel Zimroz
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Hamid Shiri: Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Pawel Zimroz: Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland

Mathematics, 2025, vol. 13, issue 12, 1-19

Abstract: Timely diagnosis and prognosis based on degradation symptoms are essential steps for condition-based maintenance (CBM) to guarantee industrial safety and productivity. Most industrial machines operate under variable operating conditions. This time-varying operating condition can accelerate the machinery’s degradation process. It may have a massive influence on data and impede the process of diagnosis and prognosis of the machinery. Therefore, in this paper, to address the mentioned problems, we introduced an approach for modelling non-stationary long-term condition monitoring data. This procedure includes separating random and deterministic parts and identifying possible autodependence hidden in the random sequence, as well as potential time-dependent variance. To achieve these objectives, we employ a time-varying coefficient autoregressive (TVC-AR) model within a Bayesian framework. However, due to the limited availability of diverse run-to-failure data sets, we validate the proposed procedure using a simulated degradation model and two widely recognized benchmark data sets (FEMTO and wind turbine drive), which demonstrate the model’s effectiveness in capturing complex non-stationary degradation characteristics.

Keywords: condition based maintenance; health index; non-stationery; degradation; time varying; Gaussian noise; Bayesian framework; long term data; autoregressive (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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