动态概率模型跟踪性能退化的实时可靠性评估方法

Real-Time Reliability Assessment Method Based on Performance Degradation Tracked by Dynamic Probability Model

针对实时可靠性评估中先验信息难以获取、分布假设不符合实际情况的问题,提出一种基于动态概率模型的实时可靠性评估方法.采用Parzen核密度估计构造滑动概率神经网络,使用滑移时间窗进行统计样本的动态选择,以非参数方式实时估计性能退化数据的条件概率分布,将超过失效阈值的分布函数值作为可靠性指标,来实现个体设备无先验信息条件下的可靠性评估.通过对高压水除鳞泵和加热炉风机失效过程数据的分析,验证了该方法的可行性和实用性.

In the process of real-time reliability assessment,acquisition of prior information is difficult and distribution hypothesis does not always conform to the actual situation,thus a real-time reliability assessment method based on dynamic probability model is presented.Taking nonparametric kernel estimation method,a moving probability neural networking is constructed,and the sliding time-window technique is used to pick statistical samples respectively and then conditional probability distribution of performance degradation data is estimated.The distribution value of performance degradation data more than failure threshold is regarded as the reliability indicator.The individual equipment reliability assessment can be accomplished without any prior information.The analysis of data from high pressure water descaling pump and heating furnace fan in the process of failure verifies the feasibility and practicability.