A life consumption monitoring methodology for electronic systems

Ramakrishnan, Arun; Pecht, Michael

doi:10.1109/tcapt.2003.817654

Cited by 130 publications

(43 citation statements)

References 11 publications

(34 reference statements)

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“…Life Consumption Models were first proposed by Ramakrishnan and Pecht (2003) for monitoring RUL in electronic systems. The LCM methodology monitors the environment of a component or system during its entire lifecycle to determine the amount of damage incurred through the various loads and conditions experienced.…”

Section: Life Consumption Modelsmentioning

confidence: 99%

“…The incurred damage is estimated through physics-of-failure models; this damage amount is related to a fraction of life lost by considering the total amount of operation under the same conditions, which would result in failure of an undamaged part. LCM is illustrated in Ramakrishnan and Pecht (2003) and Mishra et al's (2004) papers by application to a mounted printed circuit board operated under the hood of a moving vehicle. Both temperature and vibration levels were monitored on the board during use.…”

Section: Life Consumption Modelsmentioning

confidence: 99%

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Lifecycle Prognostics Architecture for Selected High-Cost Active Components

Lybeck¹,

Pham²,

Tawfik³

et al. 2011

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There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: component/system/structure selection, prognostic algorithms, and prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web-enabled tools; scalability; and standards compatibility.Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: research tools, PHM system development tools, deployable architectures, and peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A, "Assessment Criteria." Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.iv v SUMMARYAt the start of 2011, there were 439 nuclear power plants (NPPs) in the commercial global fleet; these plants started operation with 30-or 40-year licenses. The 104 NPPs in the United States represent about 10% of the installed capacity and currently provide about 20% of U.S. electricity. They have become highly efficient with an average capacity factor in excess of 91%, and they are a key element in the delivery of base-load electricity. They were originally licensed for 40 years, and a process has been established to enable license extension, to permit operation from 40 to 60 years. As of July 2011, license extensions have been granted for 71 plants at 40 sites. An additional 13 plants at nine sites are currently ...

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Section: Life Consumption Modelsmentioning

confidence: 99%

Section: Life Consumption Modelsmentioning

confidence: 99%

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

Lybeck¹,

Pham²,

Tawfik³

et al. 2011

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“…PoF-based prognostic methodologies have been applied to estimate RUL in electronic assemblies and components such as power supply chips on a DC/DC voltage converter printed circuit board (PCB) assembly 13) ; PCBs subjected to loads under an automobile hood 14), 15) ; electronics subjected to thermo-mechanical loads 16) ; and for monitoring, recording, and analyzing the life cycle vibration loads for estimation of the RUL of PCBs using cumulative damage laws. 17) A PoF-based tool has been developed for real time prediction of RUL of PCBs exposed to thermal cycling environments.…”

Section: )-12) Thementioning

confidence: 99%

A Prognostics and Health Management for Information and Electronics-Rich Systems

Pecht

2010

Journal of Japan Institute of Electronics Packaging

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“…An example of a combined prognostics architecture is the life consumption monitoring (LCM) methodology introduced by Ramakrishnand and Pecht 21 .…”

Section: Combined Prognostics Model Typesmentioning

confidence: 99%

Current Computational Trends in Equipment Prognostics

Hines¹,

Usynin²

2008

IJCIS

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The article overviews current trends in research studies related to reliability prediction and prognostics. The trends are organized into three major types of prognostic models: failure data models, stressor models, and degradation models. Methods in each of these categories are presented and examples are given. Additionally, three particular computational prognostic approaches are considered; these are Markov chainbased models, general path models, and shock models. A Bayesian technique is then presented which integrates the prognostic types by incorporate prior reliability knowledge into the prognostic models. Finally, the article also discusses the usage of diagnostic/prognostic predictions for optimal control.

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A life consumption monitoring methodology for electronic systems

Cited by 130 publications

References 11 publications

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

A Prognostics and Health Management for Information and Electronics-Rich Systems

Current Computational Trends in Equipment Prognostics

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