Signal Processing and AI Augmentation for Robust
Condition Monitoring
Abstract: The presentation highlights the critical challenges posed by asset failure and degradation, which can result in unplanned outages, reduced product quality, lower productivity, and increased operational and maintenance costs. To address these > issues, machine learning (ML)-based data-centric models are widely used for health assessment tasks such as anomaly detection, fault diagnostics, and prognostics to predict the remaining useful life of assets. However, the effectiveness of these ML models heavily relies on the quality and sensitivity of health indicators or features, which are derived using signal processing techniques. This talk will focus on two key aspects: first, the development of robust feature extraction methods using novel signal processing techniques for gear condition monitoring and second, evaluating the performance of the extracted features by comparing single-sensor data with multi-sensor fusion data for estimating the remaining useful life of assets using machine learning models.
Short Biography
Pradeep Kundu is currently working as an assistant professor in the Department of Mechanical Engineering at KU Leuven, Belgium. Before joining KU Leuven, he worked as a Post-Doctoral Fellow and Research Associate at the University of Cincinnati, USA, and > the University of Strathclyde, UK, respectively. His research focused on the broad domain of utilizing the potential of digital twins, artificial intelligence, and signal processing techniques to solve asset health management and quality control problems. > His research helps industries in reducing unplanned outages, increase productivity, automate quality control, and reduce operation and maintenance costs. He has published around 50 articles in reputed journals and conferences. He has delivered more than 20 > keynote/invited talks and has been part of more than 10 conference committees. He has received several awards, including runner-up for PHM Europe 2022 Data Challenge, overseas visiting doctoral fellowship from SERB, etc.
