摘要
加速退化试验技术已经成为评估退化失效型产品可靠性的高效手段,然而,目前对加速退化数据分析时过多依据主观经验,容易造成可靠性评估结果不准.本文提出了基于加速因子不变原则较为客观分析加速退化数据的一种方法.首先,根据加速因子不变原则推导退化模型各参数在加速退化试验中应该满足的变化规律;然后,利用与加速应力无关的参数等式辨识各加应力水平下的加速退化数据是否有效,核心是构建t统计量检验参数估值是否满足等式关系;接下来,确定与加速应力相关的参数从而实现加速退化建模;最后,利用有效的加速退化数据估计出模型参数值,外推出产品在常规应力下的可靠度.以逆高斯退化模型为例对所提方法进行了具体阐述.仿真试验和实例应用表明,本文研究为基于加速退化数据的可靠性评估提供一种更客观、合理的技术途径.
The technology of accelerated degradation testing has become an efficient approach to evaluating the reliability of the degrading product.However,the method of analyzing accelerated degradation data,which excessively dependents on subjective experience,results in the inaccuracy of the reliability evaluation.In the paper,a more objective method based on acceleration factor constant principle is proposed.First,the changing rules of the parameters of degradation models are deduced according to the acceleration factor constant principle.Next,the effectiveness of the degradation data under each accelerated stress is identified through the parameter equation independent of accelerated stress.The key is that a t statistic is constructed to verify whether the parameter estimates satisfy the parameter equation.Then,the acceleration models of the parameters dependent on accelerated stress are constructed.Last,the effective accelerated degradation data is utilized to estimate parameters,so the reliability under the normal stress level can be extraplolated.The proposed method is demonstrated by taking the inverse Gaussian process as an example.Both the simulation test and case application indicate that the study of the paper provides a more objective and reasonable technical approach to reliability evaluation based on accelerated degradation data.
作者
王浩伟
滕克难
盖炳良
WANG Hao-wei;TENG Ke-nan;GAI Bing-liang(Naval Aeronautical University,Yantai,Shandong 264001,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2018年第3期739-747,共9页
Acta Electronica Sinica
基金
山东省自然科学基金(No.ZR2016FQ03)
国家自然科学基金(No.51605487)
中国博士后科学基金(No.2016M592965)
关键词
可靠性评估
加速退化
加速因子
有效性辨识
逆高斯
reliability evaluation
accelerated degradation
acceleration factor
effectiveness identification
inverse Gaussian