Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation

Jouin, Marine; Gouriveau, Rafael; Hissel, Daniel; Péra, Marie‐Cécile; Zerhouni, Noureddine

doi:10.1109/tr.2015.2454499

Cited by 77 publications

(23 citation statements)

References 53 publications

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“…At the end of this phase, when no measurement is available and the likelihood is no longer calculated; starts the prediction phase; only the state is propagated from one stage to another using the evolution model [21].…”

Section: Particle Filtering Technique Adapted To Prognostics Purpose mentioning

confidence: 99%

Contribution in Enhancing the Remaining Useful Life Prediction in Abrupt Failures: Bearing Case

Boukra¹,

Bensafia²,

Khettab³

2019

IJIES

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For a simple and accurate prediction of the Remaining Useful Life (RUL) of a component/system, degradation-based algorithms, deployed by data-driven prognostic model, attempt to track a sensed or preprocessed feature called prognostic feature, highly correlated with fault growth. This feature should reflect the fault evolution through the entire component/system life, i.e. having a monotonic trend shape. Extracted features usually show undesirable behaviors such as fluctuation, non-monotonicity and abrupt increase at the end of the component lifecycle which hampers the accurate prediction of the RUL. We must, therefore, be addressed to the identification of new prognostic features having an obvious monotonic trend shape to enhance the prediction of the RUL. In this context, this paper attempts to address this issue by further preprocessing the extracted features in a way that the identified prognostic feature results in a smoothed and trended shape. The qualities of the identified feature are evaluated by a set of established and proposed suitability metrics. Datasets from bearings run-to-failure experiments provided by FEMTO-ST Institute -IEEE PHM 2012 challenge-were used to validate our approach. A mean percentage error of 12.18% was achieved indicating that the model worked accurately and reliably on every tested bearing.

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Section: Particle Filtering Technique Adapted To Prognostics Purpose mentioning

confidence: 99%

Contribution in Enhancing the Remaining Useful Life Prediction in Abrupt Failures: Bearing Case

Boukra¹,

Bensafia²,

Khettab³

2019

IJIES

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“…Similarly, Gaussian Process State Space Models (GPSS) are proposed in [13]. In [14], an empirical model for power ageing is proposed. The evolution of the model parameters is tracked by jointing a group of particle filters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Prognostic of Fuel Cells by Implementing Ensemble Echo State Networks in Time-Varying Model Space

Zheng

Outbib

2020

IEEE Trans. Ind. Electron.

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“…They introduced a self-healing factor after each characterization and adapted the degradation model parameters to fit the changing degradation trend. Jouin et al 11 proposed an empirical model for power degradation taking into account recovery phenomena based on different features extracted from the degradation pattern.…”

Section: Introductionmentioning

confidence: 99%

Proton exchange membrane fuel cell remaining useful life prognostics considering degradation recovery phenomena

Zhang

Cadet

Yousfi-Steiner

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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This work explores the challenges of handling the recovery phenomena in the degradation behavior of the proton exchange membrane fuel cells, from the perspective of the prognostics. An adaptive prognostics and health management approach with additional knowledge, such as the electrochemical impedance spectroscopy, from the state of health characterization, is applied on two fuel cell stacks under both stationary and quasi-dynamic operating regimes. Some improvements in the prognostic performance are obtained in the view of the remaining useful life predictions by comparing with a classical particle filtering-based prognostic approach.

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Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation

Cited by 77 publications

References 53 publications

Contribution in Enhancing the Remaining Useful Life Prediction in Abrupt Failures: Bearing Case

Contribution in Enhancing the Remaining Useful Life Prediction in Abrupt Failures: Bearing Case

Adaptive Prognostic of Fuel Cells by Implementing Ensemble Echo State Networks in Time-Varying Model Space

Proton exchange membrane fuel cell remaining useful life prognostics considering degradation recovery phenomena

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