Condition monitoring (CM) data should undergo through preprocessing to extract health indicators (HIs) for proper system health assessment. Machine health indicators provide vital information about health state of subcomponents(s) or overall system. There are many techniques in the literature used to construct HIs from CM data either for failure diagnostics or prognostics purposes. The majority of proposed HI extraction methods are mostly application specific (e.g. gearbox, shafts, and bearings etc.). This paper provides an overview of the used techniques and proposes an HI extraction, evaluation, and selection framework for monitoring of different applications. The extracted HIs are evaluated through a compatibility test where they can be used in either failure diagnostics or prognostics. An HI selection is carried out by a new hybrid feature goodness ranking metric in feature evaluation. The selected features are then used in fusion to get the representative component HI. Several case study CM data are used to demonstrate the essentiality of the proposed framework in component monitoring.

状态监控（CM）数据应经过预处理，以提取健康指标（HIs）以进行正确的系统健康评估。机器运行状况指示器提供有关子组件或整个系统的运行状况的重要信息。文献中有许多技术可用于从CM数据构建HI，以用于故障诊断或预测目的。大部分建议的HI提取方法主要针对特定​​应用（例如齿轮箱，轴和轴承等）。本文概述了所使用的技术，并提出了一种HI提取，评估和选择框架，用于监视不同的应用程序。提取的HI通过兼容性测试进行评估，可将其用于故障诊断或预测。HI选择是通过在特征评估中使用新的混合特征优度等级度量来执行的。然后将选定的特征用于融合以获得代表性分量HI。几个案例研究CM数据被用来证明所提出的框架在组件监视中的重要性。