Prof. Michael G. Pecht


E-Mail: pecht@calce.umd.edu
Office: (301) 405-5323


Prof Michael Pecht is a world renowned expert in strategic planning, design, test, and risk assessment of electronics and information systems. Prof Pecht has a BS in Physics, an MS in Electrical Engineering and an MS and PhD in Engineering Mechanics from the University of Wisconsin at Madison. He is a Professional Engineer, an IEEE Fellow, an ASME Fellow, an SAE Fellow and an IMAPS Fellow. He is the editor-in-chief of IEEE Access, and served as chief editor of the IEEE Transactions on Reliability for nine years, and chief editor for Microelectronics Reliability for sixteen years. He has also served on three U.S. National Academy of Science studies, two US Congressional investigations in automotive safety, and as an expert to the U.S. Food and Drug Administration (FDA). He is the founder and Director of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland, which is funded by over 150 of the world's leading electronics companies at more than US$6M/year. The CALCE Center received the NSF Innovation Award in 2009 and the National Defense Industries Association Award. Prof Pecht is currently a Chair Professor in Mechanical Engineering and a Professor in Applied Mathematics, Statistics and Scientific Computation at the University of Maryland. He has written more than twenty books on product reliability, development, use and supply chain management. He has also written a series of books of the electronics industry in China, Korea, Japan and India. He has written over 700 technical articles and has 8 patents. In 2015 he was awarded the IEEE Components, Packaging, and Manufacturing Award for visionary leadership in the development of physics-of-failure-based and prognostics-based approaches to electronic packaging reliability. He was also awarded the Chinese Academy of Sciences President's International Fellowship. In 2013, he was awarded the University of Wisconsin-Madison's College of Engineering Distinguished Achievement Award. In 2011, he received the University of Maryland's Innovation Award for his new concepts in risk management. In 2010, he received the IEEE Exceptional Technical Achievement Award for his innovations in the area of prognostics and systems health management. In 2008, he was awarded the highest reliability honor, the IEEE Reliability Society's Lifetime Achievement Award. He has previously received the European Micro and Nano-Reliability Award for outstanding contributions to reliability research, 3M Research Award for electronics reliability analysis, and the IMAPS William D. Ashman Memorial Achievement Award for his contributions in reliability assessment methods for electronics products and systems.

Professor Michael Pecht's research focuses on prognostics and systems health management (PHM) using machine learning. PHM is an approach that is used to evaluate the reliability of a system in its actual life-cycle conditions, determine the initiation of failure, and mitigate system risks. Prognostics of a system can yield an advance warning of impending failure in a system and thereby help in maintenance and corrective actions.. The outputs of a prognostic assessment of a product are the failure risk, time to failure, remaining useful life, and a prognostic distance within which time specific maintenance and repair actions can be taken to extend the life of the product.. The U.S. Joint Strike Fighter (JSF) Program requires PHM. NASA uses the Integrated Vehicle Health Management (IVHM) program for its fleet. Consumer electronics companies, including computer companies such as Dell, are investing a lot of money in prognostics research so that they can harness the benefits of PHM for reducing warranty costs and cutting product qualification time. The data-driven and fusion approaches stand among the three main approaches to implementing prognostics for a system (along with model-based). The data-driven prognostics methods use current and historical data to statistically and probabilistically derive decisions, estimates, and predictions about the health and reliability of products. Data-driven approaches are useful to monitor the health of large multivariate systems and are capable of intelligently detecting and assessing correlated trends in the system dynamics to estimate the current and future health of the system. Areas of interest for data-driven approaches include anomaly detection, fault identification, fault isolation and prediction of remaining useful life (prognostics). Machine learning is highly used in the data-driven approach since it incorporates statistical and probability theory in addition to data preprocessing, dimensionality reduction by compression and transformations, feature extraction, and cleaning (de-noising) of data. Fusion methods for prognostics offer the benefits of model-based and data-driven methods.