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.

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.