Prognostic enhancements to gas turbine diagnostic systems

Byington, Carl S.; Watson, Matthew J.; Roemer, Michael; Galic, T.R.; McGroarty, Jack J.

doi:10.1109/aero.2003.1234168

Cited by 24 publications

(14 citation statements)

References 2 publications

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“…This theme becomes a subject of major industrial research as mentioned in recent papers on the Condition Based Maintenance (CBM) [1][2][3]. Three approaches to prognostics can be distinguished [3][4]: model based [5], data -based [6] and hybrid based [7]. The data based approach aims at estimating the behavior of the process without needing deep knowledge of all the physical phenomena of the system.…”

Section: Introductionmentioning

confidence: 98%

Fuel Cells Remaining Useful Lifetime Forecasting Using Echo State Network

Morando

Jemeï

Gouriveau

et al. 2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

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The Hydrogen energy vector is one of the possible solutions to overcome future energy crisis announced by the International Energy Agency. However, various bottleneck, whether technological or societal, slow the industrial interest for this technology and therefore the mass production of fuel cells. Among these locks that may be mentioned one relating to the still limited useful lifetime of the fuel cells. To improve this lifetime, one of the existing approaches is to use the discipline of PHM (for Prognostics and Health Management). This discipline aims to improve the efficiency of control and maintenance operations on the system by using diagnostic or prognostics algorithms. This article covers the prognostics aspect of PHM applied to a PEMFC using an algorithm based on a tool from the reservoir computing discipline to predict the Remaining Useful Lifetime.

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Section: Introductionmentioning

confidence: 98%

Fuel Cells Remaining Useful Lifetime Forecasting Using Echo State Network

Morando

Jemeï

Gouriveau

et al. 2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

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“…According to the literature, three classes of prognostics approaches are commonly distinguished [2]. 1) Model-Based prognostics aim at obtaining a physical model of the system's behavior [3]. This kind of approaches involves a high level of knowledge and supposes that the ageing process can be formalized into a mathematical form.…”

Section: Introductionmentioning

confidence: 99%

Fuel Cells prognostics using echo state network

Morando

Jemeï

Gouriveau

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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One remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process' behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where realtime complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising.

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“…Prognosis, as an essential element of any condition-based maintenance (CBM) system, is the prediction of future health states and failure modes based on current health assessment, historical trends and projected usage loads on the equipment and/or process [1] [2]. Thus, based on the health condition or degradation of equipment, a fault prognosis algorithm can be developed to estimate the remaining useful life (RUL).…”

Section: Introductionmentioning

confidence: 99%

An integrated hybrid methodology of time series forecast and case-based reasoning for fault prognosis

Viana

Góes

Rocha³

2013

2013 IEEE Conference on Prognostics and Health Management (PHM)

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This paper presents a methodology for system prognosis based on indicative parameter time series of the equipment condition. The time series is divided in different candidate scenarios according to modifications on exogenous variables that represent external environmental conditions. Each valid scenario is associated with a specific progression model built based on ARIMA time series analysis approach. The forecast model is determined by merging the current scenario progression model with the progression model associated with most similar past scenario. The feasibility and effectiveness of the approach proposed is demonstrated through the prediction of the degradation characteristics provided by DC machine benchmark fault simulator. Time series; Case-Based Reasoning; DC Motors; Fault PrognosisI.

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Prognostic enhancements to gas turbine diagnostic systems

Cited by 24 publications

References 2 publications

Fuel Cells Remaining Useful Lifetime Forecasting Using Echo State Network

Fuel Cells Remaining Useful Lifetime Forecasting Using Echo State Network

Fuel Cells prognostics using echo state network

An integrated hybrid methodology of time series forecast and case-based reasoning for fault prognosis

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