[WiE Webinar] Signal-to-Semantics: LLM-Powered Time Series Understanding for Explainable Industrial Fault Diagnosis Paradigm
Friday 28 May 2026 at 7:00 PM CST, 1:00 PM CET, 7:00 AM ET
By Chunhui Zhao (College of Control Science and Engineering, Zhejiang University, Hangzhou, China)
Extraordinary Women-in-IES Webinar
Register now using the link below:
https://attendee.gotowebinar.com/register/4914301759458674779
Abstract:
Fault diagnosis is a critical link in ensuring the safe operation of industrial systems. Traditional time-series data diagnosis models typically output abstract results, such as anomaly scores or fault categories, but they cannot answer key questions like “why the fault occurred” or “how to perform maintenance.” Although large language models (LLMs) show great potential for fault diagnosis, they face the challenge of a semantic gap when processing time-series industrial signals; that is, continuous temporal data are difficult to encode into discrete tokens that language models can effectively process. Differing from the traditional “signal-to-category” paradigm in fault diagnosis, we propose a novel explainable fault diagnosis framework, namely the “Signal-to-Semantics” (S2S) fault diagnosis framework. Our research replaces the original paradigm of mapping abstract time-series data to abstract diagnostic results, and instead outputs reasoning processes and diagnostic texts that are comprehensible and verifiable by human experts, establishing a new generation of intelligent diagnosis frameworks for industrial equipment.
Presenter’s bio:
Chunhui Zhao, Qiushi Distinguished Professor, Recipient of the National Outstanding Youth Fund. She received Ph.D. degree from Northeastern University, China, in 2009. From 2009 to 2012, she was a Postdoctoral Fellow with the Hong Kong University of Science and Technology and the University of California, Santa Barbara, Los Angeles, CA, USA. From 2012 to 2014, she was a distinguished researcher with Zhejiang University and since Dec. 2014, she has been a Professor with the College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Her research interests are artificial intelligence theory and methods for industrial applications. She has authored or coauthored more than 260 papers in peer-reviewed international journals. She has published 4 monographs and four big data textbooks. She authorized more than 90 invention patents. She is principal investigator of a Distinguished Young Scholar Program supported by the Natural Science Foundation of China. She has hosted more than 20 scientific research projects, including the NSFC funds, National key R&D project, provincial projects and corporate cooperation projects. She has been awarded multiple research accolades, including the First Prize in Natural Science of Zhejiang Province, the Natural Science Award from the Ministry of Education, the inaugural Youth Science and Technology Award of Zhejiang Province, and the First Prize in Natural Science from the Chinese Association of Automation. She has been honored with more than ten academic awards, including the China Young Female Scientist Award, the Fellow of IET, the Model Woman Pacesetter of Zhejiang Province, et al. She has served AE of multiple International Journals, including IEEE Transactions on Automation Science and Engineering, Journal of Emerging and Selected Topics in Industrial Electronics, Journal of Process Control, Control Engineering Practice and Neurocomputing, etc.