There has been a growing interest in monitoring the ongoing "health" of products and systems in order to predict failures and provide warning to avoid catastrophic failure. Here, health is defined as the extent of degradation or deviation from an expected normal condition. While the application of health monitoring, also referred to as prognostics, is well established for assessment of mechanical systems, this is not the case for electronic systems. However, electronic systems are integral to the functionality of most systems today, and their reliability is often critical for system reliability.This paper presents the state-of-practice and the current state-of-research in the area of electronics prognostics and health management. Four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads. Examples are provided for these different approaches, and the implementation challenges are discussed.
Index Terms-Built-in-test (BIT), prognostics and health management (PHM).
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______________________________________________________________________Abstract: The life-cycle environmental and usage conditions of a product or system can be monitored and analyzed to assess its on-going health, provide advance warning of failure, and provide information to improve the design and qualification of fielded and future products. The challenge lies in the implementation of this method in application conditions. This paper presents methods to effectively collect and analyze life-cycle environmental and usage data for in-situ health assessments. An integrated hardware-software micro-programmable module for health and usage monitoring of electronic products in their application environment is presented. The hardware incorporates local sensors, and on-board processing power using embedded software. These data processing capabilities are intended to enable immediate and localized processing of the raw sensor data to reduce the power and memory consumption anticipated during load monitoring. Guidelines are also provided to develop a life cycle monitoring plan, that encompasses the selection of environmental and usage parameters. A case study is presented to illustrate the methodology.
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