Identification of Failure Mechanisms to Enhance Prognostic Outcomes
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TECHNICAL ARTICLE—PEER-REVIEWED
Identification of Failure Mechanisms to Enhance Prognostic Outcomes Sony Mathew • Mohammed Alam • Michael Pecht
Submitted: 20 July 2011 / Published online: 10 September 2011 Ó ASM International 2011
Abstract 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. Keywords Failure mechanisms Precursor parameters Physics-of-failure Remaining life Fusion prognostics
Introduction The reliability of a product is defined as the ability of the product to perform its intended functions for a specific period of time in its life cycle application conditions [1]. Current reliability assessment techniques cannot account S. Mathew (&) M. Alam M. Pecht Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA e-mail: [email protected] M. Pecht Prognostics and Systems Health Management Centre, City University of Hong Kong, Hong Kong, Hong Kong
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for real-time changes in environmental and operational conditions of products in the field. Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. The prognostics of a system can yield an advance warning of impending failure in the system and thereby help in taking appropriate corrective actions. PHM can help to prevent catastrophic failures and reduce unscheduled maintenance expenses. The PHM has become the preferred approach to achieve efficient system-level maintenance and reduce the life cycle costs of systems [1]. The U.S. Department of Defense 5000.2 policy document on defense acquisition states that program managers should utilize diagnostics and prognostics to optimize the operational readiness of defenserelated systems [2]. Typically, a combination of sub-systems and parts constitutes a product. For example, a cell phone today is not just a means to talk with someone else; it can also be used to take pictures and videos, browse the web, send text messages, listen to music, and watch videos. All these functions necessitate the inclusion of different sub-systems within the cell phone. The more
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