Prediction of machine state for non-Gaussian degradation model using Hidden Markov Model approach

Joanna Janczura; Wojciech Żuławiński; Hamid Shiri; Tomasz Barszcz; Radosław Zimroz; Agnieszka Wyłomańska

doi:10.17531/ein/193898

1/2025 vol. 27

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RESEARCH PAPER

Prediction of machine state for non-Gaussian degradation model using Hidden Markov Model approach

Joanna Janczura ¹

Wojciech Żuławiński ¹

Hamid Shiri ²

Tomasz Barszcz ³

Radosław Zimroz ²

Agnieszka Wyłomańska ¹

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Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Wroclaw, Poland

Faculty of Geoengineering Mining and Geology, Wroclaw University of Science and Technology, Poland

Faculty of Mechanical Engineering and Robotics, AGH University, Kraków, Poland

Submission date: 2024-06-14

Final revision date: 2024-08-12

Acceptance date: 2024-09-29

Online publication date: 2024-10-07

Publication date: 2024-10-07

Corresponding author

Joanna Janczura

Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Wroclaw, Poland

Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):193898

DOI: https://doi.org/10.17531/ein/193898

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HIGHLIGHTS

On-line prediction method for forthcoming machine state is proposed.
Degradation model with three machine states (healthy, warning and alarm) is used.
α-stable HMM method is developed for non- Gaussian (impulsive) HI data.

KEYWORDS

hidden Markov model

α−stable distribution

prediction

machine health management

machine condition monitoring

TOPICS

Monitoring and diagnostics for maintenance of technical systems

ABSTRACT

Machinery health management becomes an essential issue in many sectors. The ultimate goal is to predict machinery degradation and accordingly plan maintenance actions. However, prediction becomes much harder if data is noisy. We propose a procedure for on-line prediction of the forthcoming machine state. This procedure is dedicated to the non-Gaussian (impulsive) health index (HI) data. It is based on a simplified degradation model with three machine states, i.e. healthy, warning and alarm, described in terms of a Hidden Markov Model (HMM). Using simulated trajectories we demonstrate that the α-stable HMM dedicated to time series with impulsive behaviour outperforms the classical Gaussian approach and can be an efficient alternative in such a case. In particular, the percentage errors of the predicted alarm state transition points decrease from 20%−45% to 1%−6%, if the α-stable HMM is used instead of the Gaussian one. We illustrate the proposed methodology for two datasets acquired during experiment on the VIBstand test rig and for a benchmark FEMTO dataset.

ACKNOWLEDGEMENTS

The work of WŻ, RZ and AW was supported by National Center of Science under Sheng2 project No. UMO2021/40/Q/ST8/00024 "NonGauMech - New methods of processing non-stationary signals (identification, segmentation, extraction, modeling) with non-Gaussian characteristics for the purpose of monitoring complex mechanical structures". The work of JJ was supported by project No. POIR.01.01.01-00-0350/21 entitled "A universal diagnostic and prognostic module for condition monitoring systems of complex mechanical structures operating in the presence of non-Gaussian disturbances and variable operating conditions" co-financed by the European Union from the European Regional Development Fund under the Intelligent Development Program. The project was carried out as part of the competition of the National Center for Research and Development no: 1/1.1.1/2021 (Szybka Ścieżka). HS gratefully acknowledges the European Commission for its support of the Marie Sklodowska Curie programme through the ETN MOIRA project (GA 955681). The work of Tomasz Barszcz was supported by Department of Robotics and Mechatronics, AGH University of Cracow

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