{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/use-digital-twins-to-support-fault-diagnosis","title":"Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data","arxiv_id":"2411.01360","date":"2024-11-02","proceeding":null,"authors":["Killian Mc Court","Xavier Mc Court","Shijia Du","Zhiguo Zeng"],"abstract":"Deep learning models have created great opportunities for data-driven fault diagnosis but they require large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from $4$ different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system.","url_abs":"https://arxiv.org/abs/2411.01360v1","url_pdf":"https://arxiv.org/pdf/2411.01360v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"use-digital-twins-to-support-fault-diagnosis","repo_url":"https://github.com/sonic160/dtr_digital_model_simulink","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"fault-diagnosis","task_name":"Fault Diagnosis"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[{"slug":"digital-twin-supported-deep-learning-for","name":"Digital twin-supported deep learning for fault diagnosis","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/fault-diagnosis-on-digital-twin-supported","task":"Fault Diagnosis","dataset":"Digital twin-supported deep learning for fault diagnosis","model":"LSTM","rank_in_archive_order":2,"of":2,"metrics":{"Accuray":"61.56"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}