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Intelligent Fault Diagnosis and Prognosis For System Health Management

Series: PROF. V.V.S. SARMA MEMORIAL LECTURE

Speaker: Prof. Krishna R. Pattipati, Distinguished Professor Emeritus and the Collins Aerospace Chair,Professor of Systems Engineering in the Department of Electrical and Computer Engineering, University

Date/Time: Jan 12 16:00:00

Location: CSA Lecture Hall (Room No. 117, Ground Floor)

Abstract:
"Good Applications (plus Math) lead to Good Theory and Algorithms." We are guided by this maxim in our research on developing and applying systems theory, optimization and inference techniques to system health management. This talk, which is intended for non-experts, presents a short overview of our contributions to, and impact on, real world applications involving Integrated Diagnostics and Prognostics.
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Efficient troubleshooting and repair procedures, both for on-board and off-board diagnosis and prognosis, help in minimizing the maintenance wait time, and in reducing and managing the spares in a supply chain management process. In this talk, I will begin with a recollection of my interactions with Prof. V.V.S. Sarma and our overlapping research interests. Then, I will discuss hybrid model-based and data driven techniques that seamlessly employ quantitative (analytical) models, machine learning techniques and graph-based cause-effect models for intelligent diagnosis and prognosis, and then discuss how we have applied these to automotive, aerospace, building, power and manufacturing systems over the years. I will emphasize the need for designing health management into an intelligent system early, and identify seven fundamental problems, viz., sensor optimization, test designs for anomaly detection, on-board diagnosis, guided troubleshooting, predictive maintenance, mission impact analysis and agile mission re-planning for managing uncertainty, that enable such systems to sense, assess, anticipate and respond either autonomously or under human supervision. The presentation will conclude with a plan to apply such process to resilient systems to address the issues of subsystem fault propagation, model uncertainty, unanticipated faults, time delays, mission re-planning and supply chain management. Efficient troubleshooting and repair procedures, both for on-board and off-board diagnosis and prognosis, help in minimizing the maintenance wait time, and in reducing and managing the spares in a supply chain management process. In this talk, I will begin with a recollection of my interactions with Prof. V.V.S. Sarma and our overlapping research interests. Then, I will discuss hybrid model-based and data driven techniques that seamlessly employ quantitative (analytical) models, machine learning techniques and graph-based cause-effect models for intelligent diagnosis and prognosis, and then discuss how we have applied these to automotive, aerospace, building, power and manufacturing systems over the years. I will emphasize the need for designing health management into an intelligent system early, and identify seven fundamental problems, viz., sensor optimization, test designs for anomaly detection, on-board diagnosis, guided troubleshooting, predictive maintenance, mission impact analysis and agile mission re-planning for managing uncertainty, that enable such systems to sense, assess, anticipate and respond either autonomously or under human supervision. The presentation will conclude with a plan to apply such process to resilient systems to address the issues of subsystem fault propagation, model uncertainty, unanticipated faults, time delays, mission re-planning and supply chain management.

Speaker Bio:
Krishna R. Pattipati is the Distinguished Professor Emeritus and the Collins
Aerospace Chair Professor of Systems Engineering in the Department of Electrical and Computer Engineering at the University of Connecticut, Storrs, CT, USA. Prof. Pattipatis research activities are in the areas of proactive decision support, autonomy, and optimization-based learning and inference. A common theme among these applications is that they are characterized by a great deal of uncertainty, complexity, and computational intractability. He has published over 500 scholarly journal and conference papers in these areas. He is a cofounder of Qualtech Systems, Inc., a firm specializing in advanced integrated diagnostics software tools (TEAMS, TEAMS-RT, TEAMS-RDS, TEAMATE, PackNGo), and serves on the board of Aptima, Inc.


Prof. Pattipati received the Centennial Key to the Future award in 1984 from the IEEE Systems, Man and Cybernetics (SMC) Society, and was elected a Fellow of the IEEE in 1995 for his contributions to discrete-optimization algorithms for large-scale systems and team decision-making. He received the Andrew P. Sage award for the Best SMC Transactions Paper for 1999, Barry Carlton award for the Best Aerospace and Electronic Systems (AES) Transactions Paper for 2000, the 2002 and 2008 NASA Space Act Awards for "A Comprehensive Toolset for
Model-based Health Monitoring and Diagnosis," and "Real-time Update of Fault-Test
Dependencies of Dynamic Systems: A Comprehensive Toolset for Model-Based Health
Monitoring and Diagnostics", the 2003 AAUP Research Excellence Award, the 2005 School of Engineering Teaching Excellence Award at the University of Connecticut, and the 2023
Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur. Prof. Pattipati served as Editor-in-Chief of the IEEE Transactions on SMC-Cybernetics (Part B) during 1998-2001.

Host Faculty: Prof. Y. Narahari