Harnessing data and AI for Fault Detection and Diagnosis in Smart Buildings by Jin Wen
The seminar discusses how faults in sensors, devices, and control strategies affect building performance and occupant well-being. Prof. Jin Wen from Drexel University will provide a deep overview of the SoTA ML/AI based FDD techniques.
The traditional rule-based FDD methods dominate current commercial tools, data-driven approaches using machine learning and AI offer greater accuracy and autonomy with less reliance on expert knowledge. The seminar presents state-of-the-art ML/AI-based FDD techniques, addresses real-world implementation challenges—such as data quality and scalability—and will provide an overview of the International Energy Agency’s ANNEX 81 (Data-driven Smart Buildings) and other ongoing projects in which the speaker is actively involved.
Jin Wen bio: She is currently the Interim Vice Dean for Research, Innovation, and Faculty Advancement of the College of Engineering, and a Professor in the Department of Civil, Architectural, and Environmental Engineering at Drexel University. She will start to serve as the Department Head for Penn State University’s Architectural Engineering Department in August 2025. She is a Fellow of American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE) and currently the Rising Chair of ASHRAE’s Research Administration Committee (RAC), which oversees and coordinates all ASHRAE research activities. She is the Task Leader for International Energy Agency (IEA)’s Energy in Buildings and Communities (EBC) Annex 81 (Data-Driven Smart Buildings) Task C (Applications). Dr. Wen was selected as the U.S. Fulbright Scholar for 2019-2020 (Sweden).
