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.

翻译：资产健康监测在生产力、可靠性和降低成本方面仍然日益重要,早期发现过失是健康管理的基石,是新出现的预测和健康管理理念的一部分,本文件建议采用隐性Markov模型来评估机器健康退化情况。考虑利用主要组成部分分析(PCA)来增强从振动信号中提取的特征。强化的特征捕捉了数据的第二顺序结构。基于携带试验床的实验结果显示了拟议方法的可信度。