Health indicator (HI), which aims to make quantitative measures for machinery operating state at different degradation stages, is very critical in machinery condition monitoring. Some HIs from different aspects have been developed and reported in recent years. However, a preferable HI which is more robust to transient interferences, free of complicated model training and also sensitive to incipient defects in machinery condition monitoring still remains to be further investigated. To address these issues, a novel HI based on signal probability distribution measures is proposed in this paper. Firstly, characteristic parameters of the alpha stable distribution are preliminarily estimated based on the machinery degradation data, the consistency of which is quantitatively evaluated and optimized through the hypothesis test with a parameter calibration strategy. Afterwards, signal distribution models are accordingly constructed to describe the statistical characteristics of the machinery degradation data. On this basis, the deviation of the established signal distribution models between the current degradation state and the initial fault-free state is accordingly analyzed and quantified for machinery degradation assessment. Experimental validations by using simulated and industrial run-to-failure datasets demonstrate that the proposed HI can effectively recognize the state shift of the machinery during the degradation process and can be therefore applied for machinery condition monitoring.

健康指标（Health Indicator，HI）旨在对机械在不同退化阶段的运行状态进行量化测量，在机械状态监测中非常关键。近年来已经开发和报道了一些来自不同方面的HI。然而，更好的 HI 对瞬态干扰更稳健，无需复杂的模型训练，并且对机械状态监测中的早期缺陷也很敏感，仍有待进一步研究。为了解决这些问题，本文提出了一种基于信号概率分布度量的新型 HI。首先，根据机械退化数据初步估计α稳定分布的特征参数，其一致性通过参数校准策略的假设检验进行定量评估和优化。之后，据此构建信号分布模型来描述机械退化数据的统计特征。在此基础上，对建立的信号分布模型在当前退化状态与初始无故障状态之间的偏差进行相应分析和量化，用于机械退化评估。通过使用模拟和工业运行到故障数据集进行的实验验证表明，所提出的 HI 可以有效地识别退化过程中机械的状态变化，因此可以应用于机械状态监测。相应地构建信号分布模型来描述机械退化数据的统计特征。在此基础上，对建立的信号分布模型在当前退化状态与初始无故障状态之间的偏差进行相应分析和量化，用于机械退化评估。通过使用模拟和工业运行到故障数据集进行的实验验证表明，所提出的 HI 可以有效地识别退化过程中机械的状态变化，因此可以应用于机械状态监测。相应地构建信号分布模型来描述机械退化数据的统计特征。在此基础上，对建立的信号分布模型在当前退化状态与初始无故障状态之间的偏差进行相应分析和量化，用于机械退化评估。通过使用模拟和工业运行到故障数据集进行的实验验证表明，所提出的 HI 可以有效地识别退化过程中机械的状态变化，因此可以应用于机械状态监测。对建立的信号分布模型在当前退化状态与初始无故障状态之间的偏差进行相应分析和量化，用于机械退化评估。通过使用模拟和工业运行到故障数据集进行的实验验证表明，所提出的 HI 可以有效地识别退化过程中机械的状态变化，因此可以应用于机械状态监测。对建立的信号分布模型在当前退化状态与初始无故障状态之间的偏差进行相应分析和量化，用于机械退化评估。通过使用模拟和工业运行到故障数据集进行的实验验证表明，所提出的 HI 可以有效地识别退化过程中机械的状态变化，因此可以应用于机械状态监测。