Prognostic and health management for avionics

Wilkinson, Chris; Humphrey, D.; Vermeire, Bert; Houston, J. Brian

doi:10.1109/aero.2004.1368149

Cited by 37 publications

(18 citation statements)

References 12 publications

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“…An avionic system is a complex system S composed of a set of heterogeneous components Comp (pieces of hardware, software) that implement a set of functions F unc relying on a set of resources R. Available knowledge about each resource R i ∈ R may be very different then several methods for prognostics have to be used. This knowledge is described into ageing models for each resource in R (Wilkinson et al (2004)). The ageing of resources is firstly initialized to the mean time to failure (mttf for short) associated to components in which they are contained.…”

Section: Prognosis and Diagnosismentioning

confidence: 99%

“…It is in charge of monitoring the system S when it is operating. The second module is a decision support module: it is in charge of deciding for maintenance actions relying on the outputs provided by the health monitoring module (Wilkinson et al (2004)). …”

Section: Health Management and Maintenance Architecturementioning

confidence: 99%

“…The parameter values β and η change the shape and the scale of the Weibull pdf. The shape parameter β is determined such that the Weibull model follows the idealized bathtub curve from reliability (Humphrey et al (2002); Wilkinson et al (2004)). In the sequel of this paper, the problem is restricted to the useful life period of any resource in R.…”

Section: Generic Representation Of Prognosis Output As Weibull Probabmentioning

confidence: 99%

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Functional Prognostic Architecture for the Maintenance of Complex Systems

Ribot

Pencolé

Combacau

2009

IFAC Proceedings Volumes

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This paper addresses the problem of the maintenance of a complex system of heterogeneous components. With the decreasing costs of sensors, it now becomes possible to really benefit of on-line adaptive prognostic methods in order to support the optimization of the maintenance actions and scheduling. In this paper, we propose a generic health monitoring architecture that encompasses the available prognostic methods and provides a common support for the maintenance decision on a complex system. The output of this architecture takes into account the composed nature of the system by not only providing component prognoses but also function prognosis. In order to provide a prognosis for the whole system, component prognoses are combined by taking into account functional dependencies in the system.

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Section: Prognosis and Diagnosismentioning

confidence: 99%

Section: Health Management and Maintenance Architecturementioning

confidence: 99%

Section: Generic Representation Of Prognosis Output As Weibull Probabmentioning

confidence: 99%

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Functional Prognostic Architecture for the Maintenance of Complex Systems

Ribot

Pencolé

Combacau

2009

IFAC Proceedings Volumes

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“…Traditional approaches to system and component reliability are to be questioned since in many engineering applications the intrinsic lifespan of components and interconnections becomes significantly shorter than that of the systems within which they are used 3 . For example, the assumptions of essentially unlimited life and constant failure rate for electronics should be reviewed.…”

Section: Motivationmentioning

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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“…Hence, it is significant to detect anomalous states of the power subsystem to ensure the system health [1,2]. Moreover, anomaly detection is the basic function of prognostics and health management (PHM) which has been applied widely in space engineering [3][4][5].…”

Section: Introductionmentioning

confidence: 99%