Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features
Machine Learning
2021-04-22 v1 Logic in Computer Science
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.
Keywords
Cite
@article{arxiv.2104.10519,
title = {Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features},
author = {Akthem Rehab and Islam Ali and Walid Gomaa and M. Nashat Fors},
journal= {arXiv preprint arXiv:2104.10519},
year = {2021}
}