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Transfer Learning for Prognostics and Health Management:Advances,Challenges,and Opportunities
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作者 Ruqiang Yan Weihua Li +5 位作者 Siliang Lu Min Xia zhuyun chen Zheng Zhou Yasong Li Jingfeng Lu 《Journal of Dynamics, Monitoring and Diagnostics》 2024年第2期60-82,共23页
As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadri... As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadriven methods.In this paper,we briefly discuss general idea and advances of various transfer learning techniques in PHM domain,including domain adaptation,domain generalization,federated learning,and knowledge-driven transfer learning.Based on the observations from state of the art,we provide extensive discussions on possible challenges and opportunities of transfer learning in PHM domain to direct future development. 展开更多
关键词 domain adaptation domain generalization federated learning knowledge-driven PHM transfer learning
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Compound Fault Diagnosis for Rotating Machinery:State-of-the-Art,Challenges,and Opportunities 被引量:4
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作者 Ruyi Huang Jingyan Xia +2 位作者 Bin Zhang zhuyun chen Weihua Li 《Journal of Dynamics, Monitoring and Diagnostics》 2023年第1期13-29,共17页
Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault ... Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers. 展开更多
关键词 fault diagnosis compound fault signal processing artificial intelligence rotating machinery
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Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis 被引量:4
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作者 Yixiao Liao Ruyi Huang +2 位作者 Jipu Li zhuyun chen Weihua Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第3期94-103,共10页
In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagno... In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain,which has been widely used for cross domain fault diagnosis.However,existing methods focus on either marginal distribution adaptation(MDA)or conditional distribution adaptation(CDA).In practice,marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence.In this paper,a dynamic distribution adaptation based transfer network(DDATN)is proposed for cross domain bearing fault diagnosis.DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy(IDMMD)for dynamic distribution adaptation(DDA),which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain.The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods. 展开更多
关键词 Cross domain fault diagnosis Dynamic distribution adaptation Instance-weighted dynamic MMD Transfer learning
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Digital twin-assisted gearbox dynamic model updating toward fault diagnosis
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作者 Jingyan XIA Ruyi HUANG +3 位作者 Yixiao LIAO Jipu LI zhuyun chen Weihua LI 《Frontiers of Mechanical Engineering》 SCIE CSCD 2023年第2期283-296,共14页
One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerous labeled training datasets to achieve satisfactory performance.Generating training data using a virtual model is a... One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerous labeled training datasets to achieve satisfactory performance.Generating training data using a virtual model is a potential solution for addressing such a problem,and the construction of a high-fidelity virtual model is fundamental and critical for data generation.In this study,a digital twin-assisted dynamic model updating method for fault diagnosis is thus proposed to improve the fidelity and reliability of a virtual model,which can enhance the generated data quality.First,a virtual model is established to mirror the vibration response of a physical entity using a dynamic modeling method.Second,the modeling method is validated through a frequency analysis of the generated signal.Then,based on the signal similarity indicator,a physical–virtual signal interaction method is proposed to dynamically update the virtual model in which parameter sensitivity analysis,surrogate technique,and optimization algorithm are applied to increase the efficiency during the model updating.Finally,the proposed method is successfully applied to the dynamic model updating of a single-stage helical gearbox;the virtual data generated by this model can be used for gear fault diagnosis. 展开更多
关键词 digital twin GEARBOX model construction model updating physical-virtual interaction
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