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Chaochao Chen
Dr. Chaochao Chen is a member of the research staff at the Center for Advanced Life Cycle Engineering (CALCE). His research areas include fault diagnosis and failure prognosis, focusing on data-driven approaches such as machine learning and statistical methods, prediction uncertainty management, prognostics and health management (PHM) software implementation, verification and validation, fault tolerant control and their applications to robotics, electronics, battery and various mechanical systems. Prior to joining CALCE, Dr. Chen spent over three years at the University of Michigan and Georgia Institute of Technology as a research fellow, working in PHM areas in collaboration with multiple organizations in industry and the military. He has published over 20 technical papers including those in IEEE Transactions on Industrial Electronics, IEEE Transactions on Instrumentation and Measurement, Expert Systems with Applications, ASME Journal of Dynamic Systems, Measurement, and Control. He has served as a session chair and been invited to give talks for several reputed international conferences. He received his PhD in Mechanical Engineering from Kochi University of Technology in 2007.
Prognostics and Health Management （预测和健康管理）
Chaochao Chen
Dr. Chaochao Chen is a member of the research staff at the Center for Advanced Life Cycle Engineering (CALCE). His research areas include fault diagnosis and failure prognosis, focusing on data-driven approaches such as machine learning and statistical methods, prediction uncertainty management, prognostics and health management (PHM) software implementation, verification and validation, fault tol...read more
Details
Prognostics and Health Management (PHM) is being widely applied in many industrial systems to ensure high system availability over their life cycle. This web seminar will present key steps of PHM: data processing, feature extraction, fault diagnostics, and failure prognostics. The fundamental algorithms, models and techniques for each step will be discussed. Time domain, frequency domain and time frequency data analysis are introduced, and the corresponding feature extraction technologies presented. Mode-based and data-driven-based approaches are described in fault diagnostics and failure prognostics.
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