Performance reliability estimation method based on adaptive failure threshold

Abstract In the process of performance reliability estimation for individual equipments, due to the lack of a large amount of empirical information and experimental data, failure thresholds and degradation models are difficult to be determined directly. To solve this problem, a novel performance degradation reliability based on an adaptive failure threshold is proposed. First, a pattern discrimination model of degradation failure, which is combined with the one-class SVM path solution algorithm, is developed to obtain nonlinear failure threshold at any time. Second, we adopt the sliding time-window technique to extract statistical samples from degradation data series respectively and establish a dynamic kernel estimation model to continuously estimate the conditional probability density function of these samples. The probability distribution which exceeds the adaptive failure threshold is regarded as a reliability indicator. We successfully apply our method to evaluate the performance reliabilities of the bearing and the high-pressure descaling pump. Results show that the method is available to adaptively yield the failure threshold and estimate reliability for individual equipments without empirical information. Moreover, the method can dynamically adjust probability density function to meet the different statistical sample to overcome the limitation of parameter distribution model.