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首页> 外文期刊>Reliability, IEEE Transactions on >Decorrelated Feature Space and Neural Nets Based Framework for Failure Modes Clustering in Electronics Subjected to Mechanical Shock
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Decorrelated Feature Space and Neural Nets Based Framework for Failure Modes Clustering in Electronics Subjected to Mechanical Shock

机译：受到机械冲击的电子中基于失效相关特征空间和神经网络的故障模式聚类框架

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摘要

Electronic systems under extreme shock and vibration environments may sustain several failure modes simultaneously. Previous experience indicates that the dominant failure modes experienced by packages in a drop and shock framework are in the solder interconnects including cracks at the package and the board interface, pad cratering, copper trace fatigue, and bulk-failure in the solder joint. In this paper, a method has been presented for failure mode classification using a combination of Karhunen Loéve transform with parity-based stepwise supervised training of a perceptrons. New is the early classification of multiple failure modes in the pre-failure space using supervised neural networks in conjunction with a Karhunen Loéve transform. The feature space has been formed by joint time frequency analysis. Because the cumulative damage may be accrued under repetitive loading with exposure to multiple shock events, the area array assemblies have been exposed to shock and feature vectors constructed to track damage initiation and progression. The error back propagation learning algorithm has been used for stepwise parity of each particular failure mode. The classified failure modes and failure regions belonging to each particular failure mode in the feature space are also validated by simulation of the designed neural network used for parity of feature space. Statistical similarity and validation of different classified dominant failure modes is performed by multivariate analysis of variance and Hotelling's T-square. The results of different classified dominant failure modes are also correlated with the experimental cross sections of the failed test assemblies. The methodology adopted in this paper can perform real-time fault monitoring with identification of a specific dominant failure mode, and is scalable to system level reliability.
机译：在极端冲击和振动环境下的电子系统可能会同时承受多种故障模式。先前的经验表明，在跌落和冲击框架中，封装所经历的主要失效模式是焊料互连，包括封装和板接口处的裂纹，焊盘缩孔，铜走线疲劳以及焊点中的大块故障。在本文中，提出了一种将KarhunenLoéve变换与基于奇偶校验的感知器逐步监督训练相结合的故障模式分类方法。新方法是使用监督神经网络结合KarhunenLoéve变换对失效前空间中的多种失效模式进行早期分类。通过联合时频分析形成了特征空间。因为累积损坏可能会在重复载荷下暴露于多个冲击事件中而产生，所以区域阵列组件已暴露于冲击波和特征向量中，以跟踪破坏的发生和发展。错误反向传播学习算法已用于每个特定故障模式的逐步奇偶校验。通过模拟用于特征空间奇偶校验的设计神经网络，也可以验证特征空间中属于每个特定故障模式的分类故障模式和故障区域。通过对方差和Hotelling的T平方进行多变量分析，可以对不同的分类主导失效模式进行统计相似性和验证。不同分类的主要失效模式的结果也与失效测试组件的实验横截面相关。本文采用的方法可以通过识别特定的主要故障模式来执行实时故障监视，并且可以扩展到系统级的可靠性。

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