Metrics and development tools for prognostic algorithms

Kacprzynski, Gregory J.; Liberson, A.; Palladino, Angelo; Roemer, Michael; Hess, Andrew; Bégin, Marc-André

doi:10.1109/aero.2004.1368198

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Of course underlying all of the remaining useful life (RUL) estimates produced by a prognostic system are stochastic processes. This revelation requires that a probabilistic sensitivity analysis be included as part of the validation and verification process of new prognostic systems (Kacprzynski et al, 2004). This also means that "saying a widget will fail in 100 hours is not sufficient.…”

Section: ) Prognostics For Maintenancementioning

confidence: 99%

A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics

Wheeler

Kurtoglu

Poll

2021

IJPHM

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One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications.

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Section: ) Prognostics For Maintenancementioning

confidence: 99%

A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics

Wheeler

Kurtoglu

Poll

2021

IJPHM

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“…The majority of publications in the field of Prognostics & Health Management (PHM) are discussing modelling, simulation and algorithms for various applications. Only very few authors have discussed the topic of validation & verification as part of a development process to an extent that can be applied to aerospace applications (Kacprzynski et. al, 2004, Leao et.…”

Section: Introductionmentioning

confidence: 99%

Development of Diagnostics & Prognostics for Condition-Based Decision Support

2014

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The market for civil and military aerospace applications shows an increasing demand for service-based contracting ("Performance Based Contracting" - PBC). These contractual-concepts are based on guaranteed performance indicators over a fixed period, enabling a share of the financial risk between the system provider and the operator. The realization of efficient condition monitoring capabilities and reliable prognostics for prediction of spares and personnel demands has been identified as one key enabling factor for a successful implementation of PBC-concepts. To ensure an optimal incorporation of the diagnostic & prognostic functions needed for this purpose, the integration has to be considered as a standard design task during the development and certification phase, rising the need to adapt existing development processes. This adaption includes the extension of certification guidelines, definition of dedicated requirements and realization of innovative verification strategies. During the last years Airbus Defence & Space was working on the definition of a development process for integration of an innovative health management strategy into new aircraft systems to support condition-based operations. Following a summary of condition monitoring and prognostic techniques, selected requirements and guidelines for development of diagnostic & prognostic functions will be presented and discussed.

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“…As the requirements are developed at the lower tiers, they tend to become more specific and, thus, independently verifiable. Early work in writing requirements for prognostic algorithms relied on basic measurements such as the confidence interval at standard mean time to failure prediction (Kacprzynski et al, 2004), average bias and precision (Roemer, Dzakowic, Orsagh, Byington, & Vachtsevanos, 2005), and minimum time to prediction and minimum improvement of the service interval over legacy methods (Line and Clements, 2006).…”

Section: Introductionmentioning

confidence: 99%

Prognostic Algorithm Verification

Clements¹,

Bodden²

2013

PHM_CONF

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A rigorous methodology is presented for both specification and verification of prognostic algorithm performance. The prognostic algorithm specification statement takes the form, “The prognostic algorithm shall provide a minimum of <TTM> hours time-to-maintenance such that between <Lower>% and <Upper>% of failures of component ABC will be avoided with <Confidence>% confidence.” The methodology is developed first for a single failure mode case and then extended to the multiple failure mode case. The case of non-prognosable failure modes is also considered. Finally, implications of this approach are presented, including pre- tabulation of confidence bounds, estimation of the minimum amount of data required to reach a given verification confidence, and a method for using a minimum confidence growth curve to account for initial low confidence in a prognostic algorithm.

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Metrics and development tools for prognostic algorithms

Cited by 5 publications

References 4 publications

A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics

A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics

Development of Diagnostics & Prognostics for Condition-Based Decision Support

Prognostic Algorithm Verification

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