Signal-Based Diagnostics by Wavelet Transform for Proton Exchange Membrane Fuel Cell

Ibrahim, Mona; Antoni, Ursula; Steiner, Nadia Yousfi; Jemeï, Samir; Kokonendji, Célestin C.; Ludwig, Bastian; Moçotéguy, P.; Hissel, Daniel

doi:10.1016/j.egypro.2015.07.708

Cited by 32 publications

(11 citation statements)

References 7 publications

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“…All simulations start with the system behaving normally and then the faults (one It is worth observing that both drift factor ν in equation (17) and threshold level shall be chosen as a trade-off between incipient fault detection and algorithm robustness [5,6,9]: indeed, to achieve the former objective, both drift factor and threshold level should be kept small, but this may lead to an increase in false alarm incidence (i.e., noise may exert a greater influence on CUSUM sequence drift); therefore, to increase algorithm robustness (i.e. reduce false alarm probability) both parameters should be increased, but the probability of missed fault could rise as well.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…On the one hand, model-based diagnosis uses a mathematical model to simulate system variables during normal operation and to generate residuals by comparing the simulated variables with those measured on the system [5,7,8,9,12,13,14]. On the other hand, signal-based approaches directly treat measured signals to extract information and define different patterns representative of variable behaviour during both normal and faulty conditions [15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

Polverino

Frisk

Jung

et al. 2017

Journal of Power Sources

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The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

Polverino

Frisk

Jung

et al. 2017

Journal of Power Sources

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“…The results from continuous and discrete wavelet transforms have been interpreted or modified in a multitude of manners. The primary methods involve (i) examining the coefficients directly , (ii) wavelet decomposition and wavelet packet decomposition , , and (iii) using the coefficients to determine the impedance of the fuel cell . Debenjak et al.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Wavelet‐based Descriptors for Fault Diagnosis of Self‐Humidified Proton Exchange Membrane Fuel Cells

Sethi

Verstraete

2020

Fuel Cells

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Fault diagnosis can help extend the lifetime of fuel cells and has been widely explored for externally humidified fuel cells. However, fault detection in self-humidified fuel cells is scarce, despite its importance. This paper explores various wavelet-based descriptors for identifying hydration levels in self-humidified stacks. Wavelet-based techniques are nonintrusive and provide concurrent examinations in time and frequency. Thus, they encapsulate health related data that can be used as health monitoring features. The specific wavelet-based techniques used here are (i) impedance obtained through the wavelet-based fast electrochemical impedance spectroscopy (EIS) and (ii) changes in wavelet energy through wavelet and wavelet packet decomposition. Results are compared based on their trends under dehydration con-ditions and the robustness of those trends across different currents and sampling rates. They are then assessed based on their predictive ability using RReliefF (Regression-ReliefF)-a regression based feature ranking tool. Based on the qualitative and quantitative analysis, descriptors are assessed in the context of creating a short-term health monitoring system for self-humidified fuel cells. While a modified version of the wavelet energies provides the best results for differentiating faults, fast EIS opens the possibility for analysis of more complex fault modes and is shown to be more robust.

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“…From above table, we may see that the when we choose the same wavelet, the elapsed time of the CWT is much longer than the WP decomposition. By using the CWT, we must choose large number scales to show the signal components [5]. The CWT just provides the fault regions, while the WP decomposition can locate the faults and calculate the relative wavelet packet energy.…”

Section: Three-level Of Wavelet Packet Transform Decomposition Of Omentioning

confidence: 99%

Wavelet Packet Transform based Fault Diagnosis for PEM FC

Ren¹,

Ni²,

Xu³

et al. 2017

Advances in Energy Engineering

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This paper adopts the wavelet packet transform to analysis the water management of the cathode of the Proton Exchange Membrane Fuel Cell (PEM FC). It analysis the wavelet packet transform based fault diagnosis method and gives the diagnosis procedure. Then the paper applies the process to the PEM FC. The wavelet packet transform based fault diagnosis consists of a signal(oxygen stoichiometry) feature extraction by multi scale decomposition and then by calculating the value of the relative wavelet packet energy( WPE R ),gets flooding fault identification and classification. This method is low cost and easier to apply to the other dysfunction of the fuel cell systems.

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Signal-Based Diagnostics by Wavelet Transform for Proton Exchange Membrane Fuel Cell

Cited by 32 publications

References 7 publications

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

A Comparative Study of Wavelet‐based Descriptors for Fault Diagnosis of Self‐Humidified Proton Exchange Membrane Fuel Cells

Wavelet Packet Transform based Fault Diagnosis for PEM FC

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