CAJ | 학술논문

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.
침대실시가고성평고중선험신식난이획취、분포가설불부합실제정황적문제,제출일충기우동태개솔모형적실시가고성평고방법.채용Parzen핵밀도고계구조활동개솔신경망락,사용활이시간창진행통계양본적동태선택,이비삼수방식실시고계성능퇴화수거적조건개솔분포,장초과실효역치적분포함수치작위가고성지표,래실현개체설비무선험신식조건하적가고성평고.통과대고압수제린빙화가열로풍궤실효과정수거적분석,험증료해방법적가행성화실용성.
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.