振动载荷下面向电子设备PHM的板级封装潜在故障分析方法

发布时间：2019-05-08 19:55

【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.
[Abstract]:Based on adaptive spectral kurtosis and kernel probability distance clustering, a potential fault feature extraction and pattern recognition method for plate level packaging under vibration load is proposed for electronic equipment fault prediction and health management (Prognostics and Health Management,PHM). Firstly, based on the principle of maximum spectral kurtosis, the strain response data of electronic components are filtered by empirical mode decomposition, and the envelope spectrum containing potential fault information is calculated and reconstructed to form fault symptom vector. Secondly, the nonlinear fault symptom data are mapped to the high dimensional Hilbert space by using the Gaussian radial basis function probability distance method, and the cluster analysis is carried out to form a class center that represents the healthy state of the board level package and each fault mode. Finally, based on the real-time monitoring of the envelope spectrum data of the board-level package, the probability distance from each center is calculated, and the state of the package fault mode is judged so as to realize the early identification of the package fault mode. Through experimental analysis, this method can effectively identify and predict the failure mode of board-level packaging, which provides a new way of thinking and means for realizing PHM of electronic equipment.
【作者单位】：空军工程大学航空航天工程学院;
【基金】：国家自然科学基金(No.51201182) 陕西省自然科学基金(No.2015JM6345)
【分类号】：TN06

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