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Predictive reliability, prognostics and risk assessment for power modules

Predictive reliability, prognostics and risk assessment for power modules

Bailey, C. ORCID: 0000-0002-9438-3879, Lu, H. ORCID: 0000-0002-4392-6562, Yin, C. ORCID: 0000-0003-0298-0420 and Ridout, S. (2008) Predictive reliability, prognostics and risk assessment for power modules. CIPS 2008: 5th International Conference on Integrated Power Electronics Systems. Proceedings, March, 11-13, 2008 Nuremberg/Germany. ETG-Fachbericht (111). VDE Verlag GmbH, Berlin-Offenbach, Germany, pp. 19-26. ISBN 9783800730896

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Abstract

This paper describes a framework that is being developed for the prediction and analysis of electronics power module reliability both for qualification testing and in-service lifetime prediction. Physics of failure (PoF) reliability methodology using multi-physics high-fidelity and reduced order computer modelling, as well as numerical optimization techniques, are integrated in a dedicated computer modelling environment to meet the needs of the power module designers and manufacturers as well as end-users for both design and maintenance purposes. An example of lifetime prediction for a power module solder interconnect structure is described. Another example is the lifetime prediction of a power module for a railway traction control application. Also in the paper a combined physics of failure and data trending prognostic methodology for the health monitoring of power modules is discussed.

Item Type: Book Section
Additional Information: [1] Invited Paper, for Session 1: Robustness Validation. Presented at the 5th International Conference on Integrated Power Electronics Systems, CIPS 2008, held 11-13 March 2008, in Nuremberg, Germany.
Uncontrolled Keywords: Power Electronic Module (PEM), predictive reliability, risk assessment
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
School of Computing & Mathematical Sciences > Department of Computer Systems Technology
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
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Last Modified: 20 Mar 2019 11:54
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/1257

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