  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.
