Theory based on HMM and Bayesian analysis for predicting the remaining useful life of tools

An Xu

doi:10.1117/12.3030226

5 June 2024 Theory based on HMM and Bayesian analysis for predicting the remaining useful life of tools

An Xu

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131630B (2024) https://doi.org/10.1117/12.3030226
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

Addressing the challenge in effectively analyzing and predicting the remaining useful life (RUL) of tools through traditional time-frequency domain signal analysis, this paper introduces a novel multi-channel feature fusion method for intelligent tool residual life prediction, based on the Hidden Markov Model (HMM) and Bayesian theory. The process begins with extracting and filtering the time series features of the signal. These selected features are then trained using HMM. Subsequently, a health factor is developed as the observation data. The final step involves leveraging Bayesian theory and MCMC (Markov Chain Monte Carlo) estimation to realize a degradation model that allows for real-time online updates. The efficacy of this method is demonstrated using the publicly available PHM2010 dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

An Xu "Theory based on HMM and Bayesian analysis for predicting the remaining useful life of tools", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131630B (5 June 2024); https://doi.org/10.1117/12.3030226

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