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cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network

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Energy and AI 2024年第3期17卷 555-568页

作者： Zhichao Gong Bowen Wang Mohamed Benbouzid Bin Li Yifan Xu Kai Yang Zhiming Bao Yassine Amirat Fei Gao Kui Jiao State Key Laboratory of Engines Tianjin University135 Yaguan RoadTianjin300350China University of Brest UMR CNRS 6027 IRDL29238BrestFrance National Industry-Education Platform for Energy Storage Tianjin University135 Yaguan RoadTianjin300350China The Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin300072China Ability R&D Energy Research Centre School of Energy and EnvironmentCity University of Hong KongKowloonHong KongChina University of Technology of Belfort-Montbéliard CNRSInstitut FEMTO-STF-90010BelfortFrance ISEN Yncréa Ouest Brest UMR CNRS 6027 IRDL29200BrestFrance

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells(PEMFC)has advanced significantly,yet performance is hindered by variations in data distributions and the requirement for extensive fault *** this study,a cross-domain adaptive health diagnosis method for PEMFC is proposed,integrating the digital twin model and transfer convolutional diagnosis model.A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis *** extract long-term time series features from the data,a temporal convolutional network(TCN)is proposed as a pre-trained diagnosis model for the source domain,with feature extraction layers that can be reused to the transfer learning *** is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults,including pressure,drying,flow,and flooding faults,with 99.92%accuracy,through the effective capture of the long-term dependencies in time series ***,a domain adaptive transfer convolutional network(DATCN)is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant *** results show that the DATCN model,tested on three different target domain devices with adversarial training using only 10%normal data,can achieve an average accuracy of 98.5%(30%improved over traditional diagnosis models).This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices,significantly reducing the reliance on extensive fault data.

关键词： Fuel cell Fault diagnosis Transfer learning Digital twins cross-domain adaptation

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