Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features

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Published Jun 29, 2021
Akthem Rehab Islam Ali Walid Gomaa M. Nashat Fors

Abstract

Asset health monitoring continues to be of increasing importance on productivity, reliability, and cost reduction. Early Fault detection is a keystone of health management as part of the emerging Prognostics and Health Management (PHM) philosophy. This paper proposes a Hidden Markov Model (HMM) to assess the machine health degradation. using Principal Component Analysis (PCA) to enhance features extracted from vibration signals is considered. The enhanced features capture the second order structure of the data. The experimental results based on a bearing test bed show the plausibility of the proposed method.

How to Cite

Rehab, A., Ali, I., Gomaa, W., & Fors, M. N. (2021). Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features. PHM Society European Conference, 6(1), 11. https://doi.org/10.36001/phme.2021.v6i1.2947
Abstract 33 | PDF Downloads 48

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Keywords

Fault Detection, Health Monitoring, Health Management, PHM, Condition Based Maintenance, PCA, HMM, Principal Component Analysis, Hidden Markov Model, Signal Processing

Section
Technical Papers