Reliability Seminar

Abstract

For critical systems, it is very important that maintenance is performed at the right moment. Too early maintenance leads to high costs and spoiling of system lifetime, while being too late causes failures, leading to reduced reliability and availability. Predictive maintenance can assist in achieving just-in-time maintenance, and nowadays data analytics and artificial intelligence are believed to fully enable this. This presentation will show that a purely data-driven approach is not always feasible, especially in the military domain. It will be demonstrated that combining data with (physical) degradation models and knowledge on system failure behavior can lead to a much stronger approach. Moreover, this also helps to develop effective health and condition monitoring systems. Finally, some case studies from various application fields will be used to demonstrate the proposed approach.   

Biography

Prof.dr.ir. Tiedo Tinga is full professor Life Cycle Management at the Netherlands Defence Academy (NLDA) in Den Helder and professor in Dynamics based Maintenance at the University of Twente. He holds a MSc and PDEng in Material Science and a PhD in computational mechanics. He has been working at the National Aerospace Laboratory NLR for 10 years as a senior scientist. In 2007 he joined the Netherlands Defence Academy, and since 2012, he combines this position with the part-time full professorship at the University of Twente.

His research focuses on improving the predictability of failures, aiming to improve preventive maintenance processes and to develop advanced predictive maintenance concepts. The research is based on understanding and modelling the physics of failure, and combined with advanced health and condition monitoring and data analysis. The research is applied to assets in various sectors of industry. Tiedo now leads research programs on maintenance at both institutes and has been (co-)supervising 15 PhD and PDEng students in the past 5 years. Tiedo has published around 60 papers in international ISI journals and conferences.

Abstract

The use of artificial intelligence (AI) methods, including machine learning, has been providing new insights in product reliability and new ways to assess and predict product reliability. This presentation will overview how AI methods have been used by the Center for Advanced Life Cycle Engineering at the University of Maryland and its sponsoring companies: to assess failures in devices; to assess changes in failure mechanisms in devices as a results of changes in environmental regulations; to monitor the health of electronics systems; to predict individual product reliability and forecast maintenance actions; and to reduce qualification time. Examples of each of these AI-based methods will discussed. 

Biography

Prof Michael Pecht (25,000+ citations, 70+ H-Index) has a BS in Physics, an MS in Electrical Engineering and an MS and PhD in Engineering Mechanics from the University of Wisconsin. He is a Professional Engineer, an IEEE Fellow, a PHM Society Life Fellow, an ASME Fellow, an SAE Fellow and an IMAPS Fellow. He served as editor-in-chief of IEEE Access for six years, as editor-in-chief of IEEE Transactions on Reliability for nine years, editor-in-chief of Microelectronics Reliability for sixteen years, and editor of Circuit World. 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. FDA. He is the Director of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland (UMd), which is funded by over 150 of the world’s leading electronics companies at more than US$6M/year. He is also a Professor in Applied Mathematics at UMd. 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 10 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 electronics reliability. He was also awarded the Chinese Academy of Sciences President’s International Fellowship. 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.

Biography

Dr. Martin Dazer completed his master’s degree in mechanical engineering at the University of Stuttgart in 2014. In 2015 Dr. Dazer began his doctorate as a research assistant in the field of reliability engineering at the Institute for Machine Components (IMA). Supported by the Knorr-Bremse AG, Dr. Dazer conducted research until the end of 2017 on simulative reliability predictions for casted components and on efficient testing methods. Since 2018, Dr. Dazer is working as the head of reliability engineering department at IMA and is also a lecturer for reliability engineering and Design of Experiments seminars as well as a consultant in the field of reliability technology, life testing and general testing methodology. In June 2019 he was awarded his PhD with his research on the topic of reliability test planning with consideration of prior knowledge from stochastic lifetime calculations.