Event Information

Graduate Course: Applications of AI in Reliability - Fall 2026 Monday, August 31, 2026 9:30 a.m. J. M. Patterson Building (JMP) 2217 For More Information: Prof. Michael Pecht pecht@umd.edu Applications of AI in Reliability: Prognostics and Systems Health Management (Fall 2026 Graduate Course) Prof. Michael G. Pecht 301-405-5323 pecht@umd.edu Dr. Michael H. Azarian 301-405-7555 mazarian@umd.edu Prof. Jay Lee 301-405-5255 leejay@umd.edu Class Timings: Mondays, 9:30 AM to 12:10 PM US Eastern Time at J. M. Patterson Building (JMP) 2217 Find the Syllabus Here Read more about the course here. Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. In recent years, PHM has emerged as a key technology that provides an early warning of failure, forecasts maintenance, and assesses the potential for life extensions. In the future, PHM will equip systems with the capability to assess their own real-time performance (self-cognizant health management and diagnostics) under actual usage conditions and adaptively enhance life cycle sustainment with risk-mitigation actions that virtually eliminate unplanned failures.  The application areas of PHM include aerospace structures and avionics, automobiles, civil structures, consumer and industrial products, defense infrastructure and medical equipment, and machine tools. This is an interdisciplinary course, and students in many areas, including aerospace, civil, electrical, and mechanical engineering, and engineering management, are welcome. Students will get the opportunity to learn the basic scientific foundations that enable PHM and work on its implementation for real-life applications through projects. Experts from industry, government, and academia will teach guest lectures in this course. Some of the topics covered in this course include: Fundamentals of Prognostics and Health Management (PHM). Internet of Things, Big Data, and Sensors for PHM. Data Pre-processing (Data Cleansing, Feature Extraction, Feature Selection, Feature Learning). Machine Learning and Artificial Intelligence for Anomaly Detection, Diagnostics, and Prognostics. PHM Cost and Return on Investment. Valuation and Optimization of PHM-enabled Maintenance Decisions. Software Tools for PHM. Predictive Maintenance. PHM Applications in Industry. For more information, contact Prof. Michael Pecht, Dr. Michael H. Azarian, and Prof. Jay Lee. This Event is For: Clark School • All Students • Graduate • Corporate