Diagnostic of fuel cell air supply subsystems based on pressure signal records and statistical pattern recognition approach

A data-driven and application-oriented diagnosis tool is developed for Fuel Cell (FC) air supply subsystems. A bench emulating a FC air line is built to study normal and abnormal operations (clogged inlet, air leakage, error in compressor speed control) and data are collected using the air pressure transducer, which is usually implemented in FC generators. A pattern recognition approach is then applied to statistical features extracted from the pressure signal. The performance of the diagnosis strategy is evaluated from confusion matrices, associated to graphs and performance indicators. Two examples of compressors, air subsystem managements, and data records are considered to examine the method portability. Best classification rates (> 95%) are obtained on test profiles, when the pressure regulation is disabled; fault stamps can thus be found in the pressure signal morphology. Regarding the frequency of data logging, both 1 kHz and 100 Hz values are found effective for fault isolations

Fuel cell diagnosis based on stack voltage multifractal analysis. The question of the method portability

Battery Tech 2016, Dubaï, EMIRATS ARABES UNIS, 08-/12/2016 - 09/12/2016In the era of renewable and clean energies, the demand for less polluting energy generation technologies has increased rapidly. Among these technologies, the Proton Exchange Membrane Fuel Cell (PEMFC) receives much attention, as it can convert the hydrogen chemical energy into electricity with high efficiency, and also produce water and heat. However, to make this technology commercially viable, some challenges still remain. Especially the extension of the fuel cell lifespan and reliability are identified as major concerns in the research and industry sectors. The lifetime and reliability objectives can notably be achieved by implementing a diagnosis tool capable of high performances, whatever the stack design and the operating environment. In this context, we propose a new tool based on the investigation of singularity measurements stamped in fuel cell stack voltage signals. Indeed, measuring local singularities on voltage signals provides suitable information about the evolving dynamics of non-stationary and non-linear processes involved in fuel cell systems. In our study, two PEMFC stacks are experimented to evaluate the portability of our diagnosis tool. The first one is an 8 cell stack designed for automotive applications and manufactured by CEA LITEN, France. The second one is an 12 cell stack dedicated to stationary application (micro combined heat and power - µCHP application). It is designed and marketed by Riesaer Brennstoffzellentechnik GmbH and Inhouse Engineering GmbH, Germany. The steps of our diagnosis strategy are the following ones: - Two PEMFC stacks are operated under a variety of conditions (nominal, and faults i.e. more or less severe deviations from the nominal conditions) using characterization testbenches developed in lab. The deviations from the nominal conditions refer either to single fault types or to combinations of different faults. - The recorded stack voltages are analyzed using a Wavelet Leader based Multifractal Analysis (WLMA) in order to identify their singularity spectra as fault signatures. - A feature selection method is used to select the most relevant singularity features and to remove the redundant ones. - The selected singularity features are classified using Support Vector Machine (SVM) classifier according to the considered operating situations (faults and combinations of faults). The obtained results show that the proposed PEMFC diagnosis tool allows identifying simple operating failures and even more complicated situations that contain several failure types, for different stack sizes, powers and technologies for different power application environments. Framework of the 'Decentralized energy production' project, directed by EFFICACITY, the French R&D Institute for urban energy transition

The question of fuel cell durability "dynamic multifractality in stack voltage signal as an indicator of PEMFC SOH"

Battery Tech 2016, International Conference on Battery ad Fuel Cell Technology, DUBAI, EMIRATS ARABES UNIS, 08-/12/2016 - 09/12/2016Fuel Cell aging monitoring and diagnosis are key-issues for scientists and industrials who intend to spread this technology. In the present work, we propose an efficient method that enables the extraction of valuable information which contains indicators on the state of health (SOH) of a studied PEM Fuel Cell (PEMFC)

Diagnosis of fuel cells using instantaneous frequencies and envelopes extracted from stack voltage signal

The work carried out aims to diagnose fuel cells with reduced instrumentation and computation times. The article describes a non-intrusive, application-oriented diagnostic tool, based on the sole measurement of the stack voltage and requiring no specific external excitation of the electrochemical generator. The adopted data-driven method relies on well-suited signal analysis techniques (fast calculations of relevant fault signatures based on envelopes and instantaneous frequencies) and information processing (pattern recognition). A wide range of operating regimes can be identified (variations in flow rates, pressures, temperatures; combinations of simultaneous faults), even when they correspond to small deviations from nominal conditions. The portability of the method has been studied on two PEMFC stacks, designed for different applications: transport and stationary. Correct classification rates close to 98% are obtained in both cases

Multifractal Analysis of Stack Voltage Based on Wavelet Leaders: A New Tool for PEMFC Diagnosis

International audienceTo achieve a fast and low cost diagnostic, we propose a new tool based on wavelet leaders in which the proton exchange membrane fuel cell (PEMFC) diagnosis is made by the observation of the one and only stack voltage. The steps of our strategy are the following ones: (i) the PEMFC is operated under a variety of conditions (nominal or severe) using a characterization test bench developed in lab. The severe operating conditions refer either to single fault types or to different combinations of faults; (ii) the recorded stack voltages are analyzed using a wavelet leader based multifractal analysis (WLMA) in order to identify their singularity spectra as fault signatures. This novel method based on leader discrete wavelet coefficients for the estimation of the singularity spectrum is a well-suited technique for non-stationary and non-linear signals; (iii) a feature selection method is used to select the most relevant singularity features and to remove the redundant ones; (iv) the selected singularity features are classified using Support Vector Machine and K-Nearest Neighbors techniques according to the considered operating situations (faults and combinations of faults)

PEMFC stack voltage singularity measurement and fault classification

The study summarized in this paper deals with non-intrusive fault diagnosis of Polymer Electrolyte Membrane Fuel Cell (PEMFC) stack. In the proposed approach, the diagnosis operation is based on the stack voltage singularity measurement and classification. To this aim, wavelet transformbased multifractal formalism, named WTMM (Wavelet Transform Modulus Maxima), and pattern recognition methods are combined to realize the identification of the PEMFC faults. The proposed method takes advantage of the non-linearities associated with discontinuities introduced in the dynamic response data resulting from various failure modes. Indeed, the singularities signature of poor operating conditions (faults) of the PEMFC is revealed through the computing of multifractal spectra. The obtained good classification rates demonstrate that the multifractal spectrum based on WTMM is effective to extract the incipient fault features during the PEMFC operation. The proposed method leads to a promising non-intrusive and low cost diagnostic tool to achieve on-line characterizations of dynamical FC behaviors

Identification de régimes de fonctionnement de piles à combustible par reconnaissance de formes à partir d'enveloppes et de fréquences instantanées issues du signal de la tension

SGE 2020, Symposium de Génie Electrique 8 JUILLET 2021, NANTES, FRANC, NANTES, FRANCE, 05-/07/2021 - 08/07/2021Le travail réalisé vise à diagnostiquer des piles à combustible avec une instrumentation et des temps de calculs réduits. L'article décrit un outil de diagnostic non-intrusif, basé sur la seule mesure de la tension du stack et ne nécessitant pas d'excitation externe spécifique du générateur électrochimique. La méthode repose sur des techniques d'analyse du signal (calculs d'enveloppes et de fréquences instantanées) et de traitement de l'information (reconnaissances de formes). Un large panel de défauts peut être identifié (variations de débits, pressions, températures ; présence de monoxyde de carbone ; combinaisons de défauts simultanés), y compris lorsqu'ils correspondent à de faibles déviations vis-à-vis du fonctionnement nominal. La portabilité de la méthode a été étudiée sur 2 stacks PEMFC, conçus pour des applications différentes : transport et stationnaire

Voltage Singularity-Based Diagnostic for PEM Fuel Cell Stack designed for Operation in ŒCHP Units

CDFC 2017, 7th International Conference on Fundamentals & Development of Fuel Cells, , STUTTGART, ALLEMAGNE, 31-/01/2017 - 02/02/2017A non-intrusive and without disturbance diagnostic strategy for a low temperature Proton Exchange Membrane Fuel Cell (PEMFC) designed to operate in ŒCHP units is proposed. The developed diagnosis strategy consists of two phases: è In the first phase, an experimental campaign is conducted on a 12 cell PEMFC stack dedicated to stationary application. It is designed and marketed by Riesaer Brennstoffzellentechnik GmbH and Inhouse Engineering GmbH, Germany. Thereby, a database is built; it includes many physical signals measured from the stack and the testbench ancilliaries such as: gas pressures, flows, emperatures... and the voltage stack signal recorded with a sampling frequency of 3kHz for nominal and faulty operating conditions. è In the second phase, the research of relevant indicators for the PEMFC State-of-Helath (SoH) is made by analyzing the stamped singularities in the stack voltage signal according to the various operating conditions

The question of fuel cell durability "dynamic multifractality in stack voltage signal as an indicator of PEMFC SOH"

Battery Tech 2016, International Conference on Battery ad Fuel Cell Technology, DUBAI, EMIRATS ARABES UNIS, 08-/12/2016 - 09/12/2016Fuel Cell aging monitoring and diagnosis are key-issues for scientists and industrials who intend to spread this technology. In the present work, we propose an efficient method that enables the extraction of valuable information which contains indicators on the state of health (SOH) of a studied PEM Fuel Cell (PEMFC)

Multifractal analysis of stack voltage based on wavelet leaders: a new tool for the on-line diagnosis of PEMFC

FDFC 2015 - 6th International Conference on Fundamentals and Development Fuel Cell, TOULOUSE, FRANCE, 03-/02/2015 - 05/02/2015Our work is devoted to the singularity strength analysis of PEMFC stack voltages with the aim of developing non-intrusive and on-line diagnosis tools. To achieve a fast and low cost diagnostic, we propose a new tool based on wavelet leaders in which the PEMFC diagnosis is made by the observation of the one and only stack voltage. The steps of our strategy are the following ones:- The PEMFC stack is operated under a variety of conditions (nominal or severe) using a characterization test bench developed in lab. The severe operating conditions refer either to single fault types or to different combinations of faults. The recorded stack voltages are analyzed using a Wavelet Leader based Multifractal Analysis (WLMA)in order to identify their singularity spectra as fault signatures. This novel method based on leader discrete wavelet coefficients for the estimation of the singularity spectrum is a well-suited technique for nonstationary and non-linear signals. A feature selection method is used to select the most relevant singularity features and to remove the redundant ones. - The selected singularity features are classified using SVM (Support Vector Machine) and KNN (KNearest Neighbors) techniques according to the considered operating situations (faults and combinations of faults). Our results show that the proposed PEMFC diagnosis tool allows identifying simple operating failure cases and even more complicated situations that contain several failure types