RESEARCH PAPER
Prediction of machine state for non-Gaussian degradation model using Hidden Markov Model approach
1
Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Wroclaw, Poland
2
Faculty of Geoengineering Mining and Geology, Wroclaw University of Science and Technology, Poland
3
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
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
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 modelα−stable distributionpredictionmachine health managementmachine condition monitoring
TOPICS
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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