针对电子设备的测试和故障识别提出了一种基于性能退化数据的缓变故障检测方法.首先,结合电子设备退化特点和试验系统的退化数据,选取合适的线性模型近似设备参数退化轨道,得出退化数据与初始数据交叉熵随时间变化的分布曲线；然后,通过与超限概率建立关系,确定故障判定的阈值；最后,利用该试验系统的退化轨迹产生时变随机样本进行仿真验证.通过仿真分析:交叉熵方法可以比较准确的检测设备的缓变故障及老化,并且相对于检测超限概率提高了检测精度,减少了运算量；同时,交叉熵直接采用样本点进行故障检测,避免了需要拟合分布曲线计算超限概率的误差；最后,分析了样本数对交叉熵的影响,说明了为了兼顾稳定性和精确度,并不是样本数越多越好.%To solve the problem of electronic equipment test and failure diagnosis, we propose a degradation failure detecting algorithm based on Cross-entropy. First, we choose appropriate linear model as trajectory based on characteristic of electronic equipment and degradation data of test system, and obtain distribution curve of cross-entropy over time with degradation data and the initial of data. Second, by establishing the relationship with the probabilities of overrun, the threshold of the failure is determined. Finally, taking advantage of degradation trajectory of the test system, we make simulation by generating random samples of time-varying value. Through simulation analysis; The cross-entropy method can accurately detect equipment failure and the aging of the slowly changing, and with respect to the detection of overrun probability, cross-entropy algorithm improves the detection accuracy and reduces the amount of computation ; Meanwhile, the cross entropy algorithm uses discrete points measured directly for failure detection, to avoid calculation errors of fitting distribution curve compared with overrun probability; Finally, we analyze the impact of the number of samples using cross-entropy algorithm, indicating that the number of samples is not better in order to take the stability and accuracy into account.
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