Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features
This work addresses early fault detection for machinery health monitoring, but it appears incremental as it combines existing methods without reporting specific performance gains.
The paper tackled bearing fault detection by combining Hidden Markov Models with PCA-enhanced vibration features, achieving plausible experimental results on a bearing test bed.
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.