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Prognostics and health management of electronics

Vichare, N.M., Pecht, M.G.·2006·IEEE Transactions on Components and Packaging Technologies

Abstract

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.

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Metadata

Authors
Vichare, N. M., & Pecht, M. G.
Venue
IEEE Transactions on Components and Packaging Technologies
Published
2006-03
Volume
29
Issue
1
Pages
222-229
ISSN
  • 1521-3331 (ISSN-L)
  • 1557-9972
OpenAlex
W4229680264
Cited by
433
References
0
FWCI
77.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Prognostics and health management of electronics. https://app.cochat.ai/papers/10.1109/tcapt.2006.870387
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Vichare, N. M., & Pecht, M. G. (2006). Prognostics and health management of electronics. IEEE Transactions on Components and Packaging Technologies. https://doi.org/10.1109/tcapt.2006.870387

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.