Edge computing-enabled machine signal processing and fault diagnosis
Distinguished Research Seminar Series
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Date
01 Dec 2025
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Organiser
Department of Industrial and Systems Engineering, PolyU
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Time
11:00 - 12:30
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Venue
CD303
Speaker
Prof. Siliang Lu
Remarks
If you have enquiries regarding E-certificate after the seminar, please contact david.kuo@polyu.edu.hk.
Summary
Edge computing is an emerging paradigm that offloads the computations and analytics workloads onto the internet of things (IoT) edge devices to accelerate the computation efficiency, reduce the channel occupation of signal transmission, and reduce the storage and computation workloads on the cloud servers. These distinct merits make it a promising tool for IoT-based machine signal processing and fault diagnosis. This presentation introduces the light-weight designed algorithms and application-specific hardware platforms of edge computing in the typical fault diagnosis procedures including signal acquisition, signal preprocessing, feature extraction, and pattern recognition. Specially, physical neural networks implemented by analog circuits are investigated. The presentation provides an insight into the edge computing framework, methods, and applications, so as to meet the requirements of IoT-based machine real-time signal processing, low-latency fault diagnosis, and high-efficient predictive maintenance.
Keynote Speaker
Prof. Siliang Lu
Professor
School of Electrical Engineering and Automation, Anhui University, PR China
Siliang Lu received the B.S. and Ph.D. degrees in mechanical engineering from the University of Science and Technology of China, Hefei, China, in 2010 and 2015, respectively. He is currently a Professor and Vice Dean of the School of Electrical Engineering and Automation, Anhui University. He is the Principal Investigator for 4 grants of National Natural Science Foundation of China, and more than 10 grants from enterprises including State Grid Corporation of China, and Chery Automobile. He has published more than 130 articles in the prestigious journals including IEEE T Systems, IEEE TII, IEEE TIE, IEEE TPEL, IEEE TTE, IEEE TEC, IEEE T Mech, IEEE TIM, IEEE IoTJ, and IEEE Sensor J. These articles have been cited more than 5000 times and the h-index is 40, in Google Scholar Database. Since 2020, he has been listed in the World's Top 2% Scientists released by Stanford University. He serves as an Associate Editor for IEEE Transactions on Instrumentation and Measurement and IEEE Sensor Journal. His current research interests include emerging analog circuit-based physical neural networks, condition monitoring and fault diagnosis of mechanical and electrical systems, signal processing, IoT and edge computing, industrial automation and robotics.
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