In this paper, a universal probability-based method is proposed for health stage division based on the feature exacted from different rolling element bearings. A new monitoring feature called Amplitude Difference of Strong and Carpet Impulse (ADSCI) is adopted to obtain the degradation information of bearings. Specifically, the high frequency information is utilized to get the degradation status, and a new impulse extractor method is developed to quantify the degree of the degradation. The Exponential Weibull distribution (EWD) is introduced to describe the probability distribution of the ADSCI feature in healthy stage and degradation stage, respectively. Then, a method named Likelihood Probability Comparison (LPC) is put forward to online detect the degradation point which is located between the healthy and degradation stage. For the LPC method, the EWD is utilized to calculate two likelihood probabilities regarding the healthy and degradation stage, respectively. The comparison result of the two likelihood probabilities is used to detect the degradation point. The performance of the proposed method is evaluated on the PRONOSTIA platform, and the comparison experiment with other features is performed. The result shows that the proposed ADSCI feature behaves stable for different bearings and the proposed LPC method is universal and effective for health monitoring of different bearings.

针对不同滚动体轴承的特点，提出了一种基于概率的健康阶段划分通用方法。采用了一种新的监视功能，称为“强脉冲幅度差和地毯冲量”（ADSCI），以获取轴承的退化信息。具体地，利用高频信息来获得劣化状态，并且开发了新的脉冲提取器方法以量化劣化的程度。引入指数威布尔分布（EWD）分别描述了ADSCI特征在健康阶段和退化阶段的概率分布。然后，提出了一种称为似然概率比较（LPC）的方法，以在线检测位于健康阶段和降解阶段之间的降解点。对于LPC方法，EWD用于分别计算关于健康阶段和退化阶段的两个似然概率。两个似然概率的比较结果用于检测退化点。在PRONOSTIA平台上评估了该方法的性能，并进行了与其他功能的对比实验。结果表明，所提出的ADSCI特征对于不同轴承表现出稳定的性能，并且所提出的LPC方法对于不同轴承的健康状况监测是通用且有效的。并进行了其他功能的比较实验。结果表明，所提出的ADSCI特征对于不同轴承表现出稳定的性能，并且所提出的LPC方法对于不同轴承的健康状况监测是通用且有效的。并进行了其他功能的比较实验。结果表明，所提出的ADSCI特征对于不同轴承表现出稳定的性能，并且所提出的LPC方法对于不同轴承的健康状况监测是通用且有效的。