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首页> 外文期刊>Electronics Packaging Manufacturing, IEEE Transactions on >Time–Frequency and Autoregressive Techniques for Prognostication of Shock-Impact Reliability of Implantable Biological Electronic Systems
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Time–Frequency and Autoregressive Techniques for Prognostication of Shock-Impact Reliability of Implantable Biological Electronic Systems

机译：时频和自回归技术对植入式生物电子系统冲击冲击可靠性的预测

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In this paper, autoregressive and time–frequency- based techniques have been investigated to predict and monitor the damage in implantable biological electronics such as pacemakers and defibrillators. The approach focuses is on the pre-failure space and methodologies for quantification of failure in electronic equipment subjected to shock and vibration loads using the dynamic response of the electronic equipment. Presented methodologies are applicable at the system-level for identification of impending failures to trigger repair or replacement significantly prior to failure. Leading indicators of shock-damage have been developed to correlate with the damage initiation and progression in under variety of stresses in electronic systems. The approach is based on monitoring critical solder interconnects, and sensing the change in test-signal characteristics prior to failure, in addition to monitoring the transient strain characteristics optically using digital image correlation and strain gages. Previously, SPR based on wavelet packet energy decomposition and the Mahalanobis distance approach have been studied by the authors for quantification of shock damage in electronic assemblies (“Solder-joint reliability in electronics under shock and vibration using explicit finite element sub-modeling,” P. Lall, Proc. 56th ECTC, May-Jun. 2006, pp. 428–435, “Life prediction and damage equivalency for shock survivability of electronic components,” P. Lall, Proc. ITherm, May–Jun., 2006, pp. 804–816). In this paper, Autoregressive (AR), wavelet packet energy decomposition, and time–frequency (TFA) techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in implantable biological electronic systems. One of the main advantages of the AR technique is that it is primarily a signal-based technique. Reduced reliance on system analysis helps avo-n-nid errors which otherwise may render the process of fault detection and diagnosis quite complex and dependent on the skills of the analyst. Results of the present study show that the AR and TFA-based health monitoring techniques are feasible for fault detection and damage-assessment in electronic units. Explicit finite-element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, package falloff, and solder ball failure.
机译：在本文中，已经研究了自回归和基于时频的技术，以预测和监视起搏器和除颤器等可植入生物电子设备中的损害。该方法的重点是故障前的空间和方法，用于利用电子设备的动态响应来量化承受冲击和振动载荷的电子设备中的故障。所介绍的方法适用于系统级，以识别即将发生的故障，以便在故障发生之前就可以触发维修或更换工作。已经开发出了电击破坏的领先指标，以与电子系统在各种应力​​下的破坏开始和进展相关。该方法的基础是监视关键的焊料互连，并在故障之前检测测试信号特性的变化，此外还使用数字图像相关性和应变计光学监视瞬态应变特性。以前，作者已经研究了基于小波包能量分解和马氏距离方法的SPR，用于量化电子组件中的冲击损伤（“使用显式有限元子模型，在冲击和振动下电子器件的焊点可靠性”，P Lall，第56届ECTC，2006年5月6日，第428-435页，“电子元件的冲击寿命的寿命预测和等效损伤”，P。Lall，ITherm公司，2006年5月至6月，pp。 804–816）。本文研究了自回归（AR），小波包能量分解和时频（TFA）技术，用于可植入生物电子系统的系统识别，状态监测以及故障检测和诊断。 AR技术的主要优点之一是它主要是基于信号的技术。减少对系统分析的依赖有助于避免错误，否则可能会使故障检测和诊断过程变得相当复杂，并取决于分析人员的技能。本研究的结果表明，基于AR和TFA的健康监测技术可用于电子单元中的故障检测和损害评估。已经开发了显式的有限元模型，并且已经模拟了各种故障模式，例如焊球破裂，封装掉落和焊球故障。

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