中文摘要：

面向电子设备故障预测与健康管理（Prognostics and Health Management,PHM）,基于自适应谱峭度与核概率距离聚类提出一种振动载荷下板级封装潜在故障特征提取与模式辨识方法.首先,基于最大谱峭度原则利用经验模态分解的方法对电子组件的应变响应数据进行滤波,计算并重构包含潜在故障信息的包络谱形成故障征兆向量;其次,应用高斯径向基核函数概率距离方法,将非线性故障征兆数据映射到高维Hilbert空间,对其进行聚类分析形成表征板级封装健康状态与各故障模式的类中心;最后,根据实时监测的板级封装的包络谱数据计算与各中心的概率距离,判断其所属的状态从而实现对封装故障模式的早期辨识.通过试验分析,该方法可以有效辨识与预测板级封装即将发生的故障模式,为实现电子设备PHM提供了一种新式的思路与手段.

英文摘要：

A pre-failure feature extraction and modes classification method of board-level package subjected to vibration loading is presented for prognostics and health management of electronics using adaptive spectrum kurtosis and kernel probabil- ity distance clustering. Firstly strain response data of electronic components is filtered by empirical mode decomposition meth- od based on maximum spectrum kurtosis, and fault symptom vector is developed by computing and reconstructing the envelope spectrum which contains potential fault information. Secondly nonlinear fault symptom data is mapped and clustered in sparse Hilbert space based on Gaussian radical basis kernel probability distance method. Several cluster centers are formed with the characterizations of the board-level package health state and various failure modes. Finally the current state of board-level pack- age is estimated on basis of its envelope spectrum by computing its probability distance, and the forthcoming failure mode is i- dentified before it happen. The experimental analysis demonstrate the method can recognize and predict the upcoming failure mode of board-level package effectively and serve as a new approach to achieve PHM of electronics.