Data-Driven Intelligent Approach for Condition Monitoring of HVAC/ACMV System
Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way.
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- Date: 21 Oct 2022
- Time: 11:00 AM to 12:00 PM
- All times are (UTC-05:00) Central Time (US & Canada)
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- Starts 02 September 2022 11:09 AM
- Ends 20 October 2022 11:09 PM
- All times are (UTC-05:00) Central Time (US & Canada)
- No Admission Charge
Speakers
Dr. Hasmat Malik
Biography:
Dr. Hasmat Malik (Senior Member, IEEE) is a Senior Lecturer at the Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
He served as an Assistant Professor for more than five years with the Netaji Subhas Institute of Technology (NSIT), Delhi, India; and 3.5 years as a Postdoctoral scholar jointly with the BEARS (Berkeley Education Alliance for Research in Singapore) (A research center of the University of California, Berkeley, USA) and the National University of Singapore (NUS), Singapore. He is currently a Chartered Engineer (CEng) and a Professional Engineer (PEng). He is a fellow of the Institution of Electronics and Telecommunication Engineering (IETE), a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) USA, a Life Member of the Indian Society for Technical Education (ISTE), and a member of the Institution of Engineering and Technology (IET), U.K and the Institute of Engineers (India).
He has supervised 25 master’s students and 5 Ph.D. are ongoing. He is involved in several large research and development projects. He has authored/co-authored more than 100 research articles, nine books, and 15 chapters in other books, published by IEEE, Springer, and Elsevier.
He received the POSOCO Power System Award (PPSA-2017) for his Ph.D. work for research and innovation in the area of power systems. He also received the best research papers awards from IEEE INDICON-2015, and the Full Registration Fee Award from IEEE SSD-2012, Germany.
His research interests include artificial intelligence, machine learning and big-data analytics for renewable energy, smart building and automation, condition monitoring, and online fault detection and diagnosis (FDD).