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Identification of Failure Mechanisms to Enhance Prognostic Outcomes


Predicting the reliability of a system in its actual life cycle conditions and estimating its time to failure is helpful in decision making to mitigate system risks. There are three approaches to prognostics: the physics-of-failure approach, the data-driven approach, and the fusion approach. A key requirement in all these approaches is the identification of the appropriate parameter(s) to monitor the collection of the data that can be employed to assess impending failure. This article presents the physics-of-failure approach, which uses failure modes, mechanisms, and effects analysis (FMMEA) to enhance prognostics planning and implementation. This article also presents the fusion approach to prognostics and the applicability of FMMEA to this approach. As an example, a case of generating FMMEA information, and using that to identify appropriate parameters to monitor, is presented.

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The authors would like to thank the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland, and the more than 100 companies and organizations that support its research annually. The authors thank the members of the Prognostics and Health Management Consortium (PHMC) at CALCE for providing their support for this study. The authors thank Mr. Carl Carlson (Reliasoft Corp.) for his comments and suggestions. The authors thank Mr. Mark Zimmerman (CALCE) for editing this article.

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Correspondence to Sony Mathew.

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Mathew, S., Alam, M. & Pecht, M. Identification of Failure Mechanisms to Enhance Prognostic Outcomes. J Fail. Anal. and Preven. 12, 66–73 (2012).

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  • Failure mechanisms
  • Precursor parameters
  • Physics-of-failure
  • Remaining life
  • Fusion prognostics