Health assessment and prognostics of electronic products

Gu, Jie; Lau, Daniel; Pecht, Michael

doi:10.1109/icrms.2009.5269967

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…Table 1 provides some examples of prognostics parameters that have been used in automotive PHM. For example, the bending curvature of a PCB was monitored using strain gauges from the responses of the PCB under vibration loading, which were used to calculate the interconnect strain values [15]. Damage assessment was conducted by inputting the strain values in a vibration failure fatigue model.…”

Section: Monitoring Parameters Of Automotive Electronicsmentioning

confidence: 99%

Prognostics of automotive electronics with data driven approach: A review

Ahsan

Stoyanov

Bailey

2016

2016 39th International Spring Seminar on Electronics Technology (ISSE)

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Prognostics and Health Management (PHM) is a comprehensive framework that can deal with solutions for predicting and maintaining electronic system's health. The emerging concept of PHM is increasingly considered for adoption in many engineering fields such as automotive, mechanical, electrical, industrial, aerospace and railway. PHM of electronic components and systems can offer competitive advantages by improving performance, reliability, safety, maintainability and availability. In this paper, a brief description of PHM concept, current PHM approaches, key prognostics components and corresponding monitored/sensed parameters in automotive PHM applications are presented. Software tools developed for PHM applications are also reviewed. Particular focus is given on data driven approaches for prognostics of performance and reliability of automotive electronic systems. Based on the undertaken review of state-of-art in this area, key requirements and attributes of prognostic frameworks for automotive electronics are formulated and future prognostics challenges for the sector are discussed.

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Section: Monitoring Parameters Of Automotive Electronicsmentioning

confidence: 99%

Prognostics of automotive electronics with data driven approach: A review

Ahsan

Stoyanov

Bailey

2016

2016 39th International Spring Seminar on Electronics Technology (ISSE)

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“…According to the OSA-CBM architecture, the health assessment is based on consumed data issued from different condition monitoring systems or from other health assessment modules. In that way, health assessment can be seen as the first step to manage global health state of complex systems (Gu et al, 2009). It allows to define if the health in the monitored component, sub-system or system has been degraded.…”

Section: From Collection Of Phm Systems To Fleet Integrated Phm Systemmentioning

confidence: 99%

Fleet-wide health management architecture

Monnin,

Voisin,

Leger

et al. 2011

PHM_CONF

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Large complex systems, such as power plants, ships and aircraft, are composed of multiple systems, subsystems and components. When they are considered as embedded in system operating as a fleet, it raises mission readiness and maintenance management issues. PHM (Prognostics and Health Management) plays a key role for controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. However, considering a fleet implies to provide managers and engineers with a relevant synthesis of information and keep it updated regarding both the global health of the fleet and the current status of their maintenance efforts. For achieving PHM at a fleet level, it is thus necessary to manage relevant corresponding knowledge arising both from modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme for fleet PHM management applied to marine domain.

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“…In the field of Prognostics and Health Management, Health Monitoring can be seen as the first step to manage global health state of complex systems [1]. Nevertheless, as monitoring systems are usually built around particular subsystems; they focus more on diagnostic of degradations and failures modes of these subsystems.…”

Section: Introductionmentioning

confidence: 99%

Aggregation of Health Assessment Indicators of Industrial Systems

Rizzolo¹,

Abichou²,

Voisin³

et al. 2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In the field of Prognostics and Health Management (P.H.M.), Health Monitoring can be seen as the first step to manage the global health state of complex systems. Health Monitoring of industrial systems focuses on accurately describing the health state of a system, using several equipment indicators. However, managers and maintainers have to make decisions. Such decisions can be hard to make while watching all indicators of the system simultaneously. In order to ease the decision making process, a synthetic indicator, which represent the actual system's state, can be used. In this paper, we will present an approach for building an aggregated indicator characterizing the global health state of a system. This approach was implemented on the TELMA platform (integrated TELeMAintenance platform for research and education) which simulates an industrial process.

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Health assessment and prognostics of electronic products

Cited by 9 publications

References 18 publications

Prognostics of automotive electronics with data driven approach: A review

Prognostics of automotive electronics with data driven approach: A review

Fleet-wide health management architecture

Aggregation of Health Assessment Indicators of Industrial Systems

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