We conduct a regular (virtual) colloquium with interesting talks around predictive maintenance. If you want to join, please contact Nils Jansen or Anna Hermelink
Upcoming Talks
Speaker: Prof. Pierre Dersin
Title: Characterizing RUL loss rate
Date: 03.03.2021
Time: 16:00 CET
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The “RUL loss rate”, or time derivative or the RUL (remaining useful life), measures the speed at which an asset’s condition degrades and it therefore is getting closer to failure in the absence of any preventive maintenance action.
The average RUL loss rate is the derivative of the “mean residual life” (MRL) ; therefore understanding the latter’s properties is of potential interest for maintenance policy optimization.
First we study a special class of time-to-failure distributions: those for which the MRL is a linear function of time, i.e. the average RUL loss rate is constant. This class contains very well known special cases: the exponential distribution, for which the MRL is constant (equal to the MTTF), and therefore the RUL loss rate is equal to 0; at, at the other extreme, what we call the Dirac distribution, for which the asset’s lifetime is deterministic, and the loss rate is equal to 1: for every hour that passes, the remaining useful life decreases by one hour.
In general, for that family of distributions, which we characterize explicitly, the average RUL loss rate takes a value between 0 and 1. For instance, the uniform distribution is characterized by an average RUL loss rate of one half.
It is shown that, for that special family, the average RUL loss rate can be obtained explicitly as a (decreasing) function of the coefficient of variation of the time to failure. A closed-form expression for the confidence interval for the RUL is obtained.
Then those results are generalised in two directions:
◼ 1) by introducing a nonlinear time transformation that allows for transposing to some classes of time-to-failure distributions ( such as Weibull or gamma) the results obtained in the special case.
◼ 2) by considering concurrent degradation modes; for instance, the combination of a mode with constant failure rate (exponential distribution) and a mode with deterministic life time ( Dirac distribution);
Implications for maintenance policy are discussed.
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Speaker: Prof. John Andrews
Title: Next Generation Prediction Methodologies and Tools for Engineering Risk Assessment
Date: 07.04.2021
Time: 16:00 CET
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Risk Assessments performed on systems across many industrial sectors employ techniques such as Fault Tree Analysis and Event tree analysis which have their foundations back in the 1960/1970s. Since that time technology has advanced and system designs, their operating practice and maintenance strategies are now significantly different to those of the 1970s. Some of the restrictive assumptions such as: constant failure and repair rates for components, component failures being independent and the limited account of maintenance and renewal options in the component failure models employed, reduce the effectiveness of these methodologies to represent modern day systems.
In addition, research into the risk prediction techniques has made considerable advances in their capabilities since the 1970s but these advances tend to have addressed each deficiency in isolation. Examples of significant advances are the Binary Decision Diagram (BDD) method of solving Fault Tree structures, improving both accuracy and efficiency, and the Petri net method which has been proven to be an effective means of predict system performance when complex maintenance and renewal strategies are employed.
This presentation will describe the motivation, progress and methodologies used on a project to develop the next generation of risk assessment methods which is funded by Lloyd’s Register Foundation. The project aims to update the current risk assessment capabilities using a hybrid approach of methods including BDDs and Petri nets. This research addresses the deficiencies in the current approaches and extends their capabilities to better represent systems employed across the industries. The approaches developed will use the familiar causality structures of fault tree and event tree analysis, removing their traditional assumptions by changing the analysis methodologies employed.
Industrial partners from the nuclear, aerospace and railway industries are collaborating on the project to ensure it meets their requirements.
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Speaker: PDEng MSc. Frank Grooteman 12th of May 2021
Title: Probabilistic analyses and its application to life predictions of aircraft structures
Date: 12.05.2021
Time: 16:00 CET
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Many aircraft are designed according to the deterministic damage tolerance philosophy to predict the crack growth life of the structure. Alternatively, a probabilistic damage tolerance analysis can be performed, called a structural risk analysis (SRA), taking into account all important scatter sources, such as, the initial flaw size, the inspection quality, the inspection scheme, the variability in loads and crack growth material properties, instead of using scatter and safety factors. For new military aircraft, SRA is mandatory. For current military aircraft, it already has become a valuable tool for fleet management, since it offers a risk (probability of failure) development over time, which cannot be obtained from the traditional deterministic damage tolerance analysis. By this, it better signals fleet management when to take corrective (maintenance) actions to prevent (critical) failures of the aircraft.
This presentation will address the general concept of probabilistic analyses and its more specific implementation in the field of probabilistic fracture mechanics (SRA) using available fleet data. The approach will be supported by a number of examples.
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Speaker: Bart Pollmann & Dr. Wieger Tiddens
Title: Smart use of sensor data leads to modern maintenance support in future ships
Date: 02.06.2021
Time: 16:00 CET
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The Royal Netherlands Navy is planning to introduce several new ship classes within the next decade. The new ships will be technologically much more advanced than the ships currently in service. At the same time the operational availability needs to increase and the crew size needs to get smaller. This combination leads to the necessity not only to automate many functions on board, but also to increase the level of support to the maintenance organization. Sensor data can be used to better predict failures of machinery in order to allow the maintenance organization to perform timely repair actions and prevent catastrophic failures. The developments include the reuse of available data used for Monitoring & Control purposes, the introduction of extra sensor technology to better detect failure modes that cannot be detected with current systems, and the introduction of AI and Machine Learning. The Royal Netherlands Navy is cooperating with Industry to ensure the timely availability of these new methods and technologies.
Artikel Total Industrial Maintenance februari 2021
Artikel Maritiem Nederland januari 2021
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Speaker: Dr. Philippe van de Calseyde
Title: An introduction to behavioral operations management
Date: tbd
Time: 16:00 CET
Human beings are critical to the functioning and performance of the majority of operating systems. However, human behavior traditionally has been ignored in the field of operation management (OM). More specifically, most models in OM assume that agents who participate in operating processes are either fully rational or can be induced to behave rationally. That is, these models assume that people have stable preferences, are not affected by cognitive biases or emotions, and have the ability to disregard irrelevant information by only responding to relevant information when making decisions. The field of Behavioral Operations Management (BOM) departs from these idealized assumptions by acknowledging that human decision-makers are guided by emotions, cognitive biases or irrelevant situational cues when making decisions. The goal of this talk is to introduce this field by discussing the results of a research project that we recently initiated in the field of sales forecasting.
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Past Talks
Speaker: Prof. dr. Olga Fink
Title: Domain adaptation and hybrid algorithms for intelligent maintenance systems
Date: 03.02.2021
Time: 16:00 CET
The talk will give some insights into potential solutions that enable to 1) transfer models and operational experience between different units of a fleet and between different operating conditions also in unsupervised setups where data on faulty conditions is not available; and 2) fuse physical performance models and deep neural networks, thereby not only improving the performance but also improving the interpretability of the developed models.
Video
Speaker: Dr. Alessandro Di Bucchianico
Title: Statistical Process Control and Predictive Maintenance
Date: 20.01.2021
Time: 16:00 CET
The slides are available here.
AbstractIn this talk, we give a brief overview of the standard procedures in SPC, applications fields and position SPC in the wider data science context. We will discuss an industrial case study with wind turbines, that illustrates the methodological challenges in the field.
The wind turbine case study arose in a maintenance context. We will discuss benefits of including SPC in predictive maintenance strategies and highlight some methodological challenges and how they could fit within the PrimaVera context.
Video
Speaker: Dr. Ayse Sena Eruguz
Title: Maintenance Optimization for Multi-Component Systems with a Single Sensor
Date: 09.12.2020
Time: 16:00 CET
Video
Speaker: Dr. Zaharah Allah Bukhsh
Title: Decision support methods for infrastructure maintenance
Date: 25.11.2020
Time: 16:00 CET
Video
Speaker: Prof. dr. Henk Akkermans
Title: Business models for CBM-driven smart services
Date: 11.11.2020
Time: 16:00 CET
Video
Lecture Primavera Consortium Nov 11 2020 (002)
Speaker: Prof. dr. Frits Vaandrager
Title: Finding Software Bugs Using Active Automata Learning
Date: 30.09.2020
Time: 16:00 CET
Video
Speaker: Dr. B. (Bram) de Jonge
Title: Condition-based maintenance optimization based on matrix algebra
Date: 16.09.2020
Time: 16:00 CET
Bio
Speaker: Dr. Alieh Alipour
Title: Part I: Asset Management & PdM, part II: Vibration analysis, part III: Structure of the deliverable
Date: 02.09.2020
Time: 16:00 CET
Bio
Video
Speaker: Dr. Stella Kapodistria
Title: Integrated learning and decision making
Date: 08.07.2020
Time: 16:00 CET
The slides are available here.
AbstractBio
You can check out Stella’s scientific work here <https://www.tue.nl/en/news/features/making-complex-decisions-with-the-help-of-ai/> and here <https://research.tue.nl/en/persons/stella-kapodistria>.
Video
Speaker: Prof. dr. Tom. M. Heskes
Title: Causal inference and discovery
Date: 24.06.2020
Time: 16:00 CET
The slides are available here.
AbstractBio
Video
Speaker: Dr. A.J.J. (Jan) Braaksma
Title: Advanced maintenance Concepts and the use of RCM/FMEA
Date: 10.06.2020
Time: 16:00
The slides are available here.
AbstractFMEA/RCM is a structured method which can be used to determine a maintenance concept based on the ways in which an asset can possibly fail and the impacts these so-called “failure modes” can possibly have. I will discuss the development and improvement of maintenance concepts over time and the need for the right asset information. I will have special attention for its usage for the identification of possible predictive maintenance candidates and criticality driven asset information management. The organization and management of (future) data collection is advocated as a crucial element for making better maintenance decisions.
During the colloquium I will go specifically into insides gained from a multiple case study on the use of FMEA in industry and insights I gained from working together with industry.
Bio
Jan Braaksma is an associate professor in the chair of Maintenance Engineering at the University of Twente and the director of the WCM Summer School part of World Class Maintenance. He has worked for the University of Groningen (RuG) and the Dutch Defense Academy (NLDA). He holds a Master’s degree in Business and ICT and a PhD degree in Economics and Business. Jan’s research focuses on Asset Management with a special attention for Asset Life Cycle Planning, Maintenance Engineering and Design for Maintenance.
A significant part of his research is in cooperation with companies and organisations such as Liander, Strukton Rail, Netherlands Railways (NS), Prorail, Heineken, AkzoNobel, Sitech, Huntsman, Heijmans, Sabic, TataSteel and the Ministry of Defense. Jan is responsible for the Master Class on Maintenance Engineering & Management provided by the University of Twente.
Jan is involved in WP5 in PRIMAVERA.
https://www.utwente.nl/en/et/dpm/me/staff/braaksma/
https://primavera-project.com/
Video
Speaker: Prof. dr. Mariëlle Stoelinga
Title: Predictive Maintenance via Fault Tree Analysis and Model Checking
Date: 27.05.2020
Time: 16:00
The slides are available here.
AbstractI will also go into recent developments on learning fault trees and rare event simulation.
Bio
Video
Speaker: Prof. dr. ir. Tiedo Tinga
Title: Predictive maintenance, why and how?
Date: 13.05.2020
Time: 15:00
The slides are available here.
AbstractBio
Video
Speaker: Prof. dr. Geert-Jan van Houtum
Title: Predictive maintenance: Successes and challenges for the next 10 years
Date: 29.04.2020
Time: 16:00
The slides are available here.
AbstractBio
His research is focused on the maintenance and reliability of capital goods, and in particular on: (i) Design and control of service supply chains; (ii) Maintenance concepts, in particular predictive maintenance; (iii) Design for availability. He has over 80 publications in international refereed journals such as Operations Research, Manufacturing and Service Operations Management, IIE Transactions, and European Journal of Operational Research. He is area editor at Service Science and associate editor at Manufacturing and Service Operations. Much of his research is in cooperation with the industry. He works with companies such as ASML, Canon, Dutch Railways, Philips, Marel, the Royal Dutch Airforce, the Royal Dutch Navy, Thales, and Vanderlande. He is vice-dean IE of the Department IE&IS since September 2017. Further, he is a board member of the Service Logistics Forum.
For a list of publications, see: https://research.tue.nl/en/persons/geert-jan-jan-van-houtum/publications/
Video