电子学报 ›› 2016, Vol. 44 ›› Issue (4): 944-951.DOI: 10.3969/j.issn.0372-2112.2016.04.027

• 学术论文 • 上一篇    下一篇

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

汤巍, 景博, 黄以锋, 盛增津, 焦晓璇   

  1. 空军工程大学航空航天工程学院, 陕西西安 710038
  • 收稿日期:2014-11-20 修回日期:2015-03-29 出版日期:2016-04-25 发布日期:2016-04-25
  • 作者简介:汤 巍 男,1987年出生,河北保定人,空军工程大学航空航天工程学院 控制科学与工程专业博士研究生,主要研究方向为故障诊断、预测与健康管理. E-mail:rk1019@163.com;景 博 女,1965年出生,河北邯郸人,空军工程大学教授,博士生导师.主要研究方向为故障诊断与容错控制、测试性设计与验证、信息物理融合系统.
  • 基金资助:

    国家自然科学基金(No.51201182);陕西省自然科学基金(No.2015JM6345)

Latent Fault Analysis of Board-Level Package for Electronics PHM Subjected to Vibration

TANG Wei, JING Bo, HUANG Yi-feng, SHENG Zeng-jin, JIAO Xiao-xuan   

  1. Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, Shaanxi 710038, China
  • Received:2014-11-20 Revised:2015-03-29 Online:2016-04-25 Published:2016-04-25

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.

关键词: 板级封装, 故障预测与健康管理, 谱峭度, 核概率距离聚类, 振动载荷

Abstract:

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 probability distance clustering.Firstly strain response data of electronic components is filtered by empirical mode decomposition method 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 package is estimated on basis of its envelope spectrum by computing its probability distance,and the forthcoming failure mode is identified 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.

Key words: board-level package, prognostics and health management (PHM), spectrum kurtosis, kernel probability distance clustering, vibration loading

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