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: Dr. Ayse Sena Eruguz

Title: Maintenance Optimization for Multi-Component Systems with a Single Sensor

Date: 09.12.2020
Time: 16:00 CET

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We consider a multi-component system in which a condition parameter is monitored by a single sensor. Monitoring gives the decision maker some information about the system state, but it does not reveal the exact state of the components. The decision maker infers a belief about the exact state from the current condition signal and the past data, in order to decide when to intervene for maintenance. A maintenance intervention consists of a complete and perfect inspection followed by component replacement decisions. We model this problem as a partially observable Markov decision process. We consider a deterioration process that suitably reflects the deterioration characteristics of a multi-component system and a probabilistic relation between system states and condition signals. Under reasonable conditions, we investigate the structure of the optimal maintenance intervention policy.

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Speaker: Prof. Alessandro Di Bucchianico

Title: Statistical Process Control and Maintenance

Date: 20.01.2021
Time: 16:00 CET

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Speaker: Prof. dr. Olga Fink

Title: tbd

Date: 03.02.2021
Time: 16:00 CET

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Speaker: Prof. Pierre Dersin

Title:

Date: 17.02.2021
Time: 16:00 CET

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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: Dr. Zaharah Allah Bukhsh

Title: Decision support methods for infrastructure maintenance

Date: 25.11.2020
Time: 16:00 CET

Abstract
Think about all the times you had to endure train delay because of an expected switch failure, flight cancellation due to malfunction in air traffic control, blocked or reduced speeds on road due to unexpected, unannounced repair of roads/bridges.  As a user, we expect the transport network to be efficient, reliable and available.  At the same time, transportation agencies are facing competing demands to optimally spend the limited budget and manage aging infrastructure. In this talk, I will highlight the research efforts that  addresses the challenges of infrastructure asset management by developing, implementing and validating the data-driven decision support methods. Specifically, I will present the case of automated damage detection of bridges using visual data and predicting maintenance need of railway switches using data from in-use business processes.

 

Video

 

Speaker: Prof. dr. Henk Akkermans 

Title: Business models for CBM-driven smart services

Date: 11.11.2020
Time: 16:00 CET

Abstract
A fundamental problem for the further adoption of ’smart’ maintenance in service triads is that costs and benefits are dislocated in time and place. When maintenance is done with IoT enabled condition-based maintenance, in so-called CBM-driven smart services, uptimes and revenues will increase for the asset owner. However, the revenues for the other stakeholders may decrease. OEMs may provide fewer spare parts and service providers may sell fewer direct maintenance hours. And so, what is obviously beneficial for the service triad as a whole does not happen because of this misalignment. Contractual arrangements are needed that overcome these misalignments, so-called performance-based contracts. This calls for an integrated perspective on costs and benefits of smart maintenance over time. Constructing formal business models of these dynamics with all the relevant stakeholders is a proven method to develop such an integrated perspective and the associated performance-based contracts. Moreover, these dynamics change over time. The challenges and opportunities in the early stages of service growth are different from those later on. In the talk, all these business model dynamics will be discussed, based on recent work with OEM eager to develop CBM-driven smart services.

 

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

Abstract
Active automata learning is emerging as an effective technique for obtaining state machine models of software and hardware systems. In this talk, I will present an overview of work in my group in which we used automata learning to find standard violations and security vulnerabilities in implementations of network protocols such as TCP, TLS, and SSH. Also, I will discuss the application of automata learning to support refactoring of legacy embedded control software, and the theoretical challenges that we face to further scale the application of automata learning techniques.

 

Video

 

Speaker: Dr. B. (Bram) de Jonge

Title: Condition-based maintenance optimization based on matrix algebra

Date: 16.09.2020
Time: 16:00 CET

Abstract
Industrial systems are in general subject to deterioration, ultimately leading to failure, and therefore require maintenance. Due to increasing possibilities to monitor, store, and analyze conditions, condition-based maintenance policies are gaining popularity. The most detailed approach for modeling condition parameters is by using continuous-time continuous-state stochastic processes. However, the resulting analysis can be quite difficult, and therefore simulation is often used. We describe an approach for discretizing continuous-time continuous-state non-decreasing deterioration processes, resulting in discrete-time Markov chains. Furthermore, we show how standard matrix algebra can be used to optimize condition-based maintenance policies, taking into account a required planning time for carrying out maintenance.

 

Bio
Bram de Jonge is assistant professor within the department of Operations of the University of Groningen. He holds an MSc degree in Econometrics, Operations Research & Actuarial Studies (cum laude) and a PhD degree, both from the University of Groningen. His main research area is maintenance planning and optimization.

 

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

Abstract
In this presentation, I will first present the important aspects of Asset Management and how predictive maintenance strategies will influences these aspects. Then I will present the steps required for vibration analysis of the bearings in rotary machines (based on Mobius Institute books). In the end,  I will briefly explain our structure for the deliverable document for PrimaVera.

 

Bio
Alieh Alipour is the lecturer of Asset Management at the Department of Mechanical Engineering and project leader of PrimaVera at the Smart Sensor Systems Lectoraat of The Hague University of Applied Science. Prior to that she was the advisor in Asset management group at Arcadis and innovator in Structural Reliability Department at TNO. Alieh Obtained her PhD degree in Civil Engineering from TU Delft (2011-2017).

 

Video

 

Speaker: Dr. Stella Kapodistria

Title: Integrated learning and decision making

Date: 08.07.2020
Time: 16:00 CET

The slides are available here.

Abstract
With the growth of ICT and the real-time flow of data, it becomes increasingly important to be able to measure complex systems, quantify their level of health and resilience, and be able to detect changes with great speed and precision. This would inherently facilitate the transition from curative, reactive actions to preventive, prescriptive actions. In this talk, I will demonstrate through several examples how data analytic techniques can be used to transform data into knowledge and actions.

 

Bio
Stella Kapodistria is assistant professor in the Department of Mathematics and Computer Science, where she is part of the Stochastics operations section. She participates in the Networks Gravitation (NWO-Zwaartekracht) project, an NWO funded initiative to build self-organizing and intelligent networks by using algorithms and stochastics. She is also part of the DeSIRE research program on resilient engineering funded by the 4TU-call “High Tech for a Sustainable Future” and she is in the think-tank of the newly established 4TU center on Resilience Engineering. She is a co-applicant in the PrimaVera (NWA-ORC) project and the Real-time data-driven maintenance logistics (NWO – “Big Data: real time ICT for logistics”) project. As her main aim in her scientific work Stella states “revolutionizing our thinking and methods to solve today’s problems”.
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.

Abstract
Discovering causal relations from data lies at the heart of most scientific research today. In apparent contradiction with the adagio “correlation does not imply causation”, recent theoretical insights indicate that such causal knowledge can also be derived from purely observational data, instead of only from controlled experimentation. In the “big data” era, such observational data is abundant and being able to actually derive causal relationships would open up a wealth of opportunities for improving science, healthcare, and possibly predictive maintenance. In this talk, I will sketch how insights from statistics and machine learning may lead to novel approaches for robust discovery of relevant causal relationships.

 

Bio
Tom Heskes, Professor of Artificial Intelligence, Institute for Computing and Information Sciences (iCIS), Radboud University Nijmegen

 

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.

Abstract
Predictive maintenance can be seen as the most rewarding maintenance strategy. Therefore currently there is much focus is on the use of this strategy, however there can be debated that predictive maintenance is not always the most feasible strategy.

FMEA/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
Dr. Jan Braaksma (Director WCM Summer School/University of Twente)

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.

Abstract
Predictive maintenance is a promising technique that aims at predicting failures more accurately, so that just-in-time maintenance can be performed, doing maintenance exactly when and where needed. Thus, predictive maintenance promises higher availability, fewer failures at lower costs. In this talk, I will advocate a combination of model-driven (esp fault trees) and data analytical techniques to get more insight in the costs versus the system performance (in terms of availability, reliability, remaining useful lifetime) of maintenance strategies. I will show the results of three case studies from railroad engineering namely rail track (with Arcadis), the HVAC (heating, ventilation, airco; with NS).
I will also go into recent developments on learning fault trees and rare event simulation.

 

Bio
Prof. dr. Mariëlle Stoelinga is a professor of risk management, both at the Radboud University Nijmegen, and the University of Twente, in the Netherlands. Stoelinga is the project coordinator on the PrimaVera, a large collaborative project on Predictive Maintenance in the Dutch National Science Agenda NWA. She also received a prestigious ERC consolidator grant Stoelinga is the scientific program leader of the Risk Management Master, a part-time MSc program for professionals.She holds an MSc and a PhD degree from Radboud University Nijmegen, and has spent several years as a post-doc at the University of California at Santa Cruz, USA.

 

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.

Abstract
In this webinar a general introduction to (predictive) maintenance will be given. First the basic motivation for maintenance will be presented and an overview of various maintenance policies will be given. Then the more advanced policies, based on condition monitoring and prognostics will be discussed in more detail. The various options (model-based, data-driven) will be shown and the current status and challenges will be sketched. Finally, some case studies from our research projects will be used to demonstrate the potential and limitations

 

Bio
Tiedo Tinga is a professor in Life Cycle Management at the Netherlands Defence Academy (NLDA) and in Dynamics based Maintenance at the faculty of Engineering Technology of the University of Twente. He has a background in Materials Science and Mechanical Engineering. Before joining academia, he has been working as a scientist at the National Aerospace Laboratory NLR for almost 10 years. His research focuses on the detection and prediction of failures in systems, using combinations of the physics of failure, thorough understanding of the (dynamic) system behavior, advanced monitoring techniques and data analysis. All his research projects are in close collaboration with industrial partners.

 

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.

Abstract
About 10 years ago, we started with predictive maintenance research in the Netherlands. In my projects, we studied systems in the high-tech, maritime, and chemical industry.  In this presentation, I present a general predictive maintenance approach that works for systems where a limited set of components causes most of the failures. This approach builds on stochastic processes, data mining, Bayesian learning, machine learning, and Operations Research techniques. We will also discuss what we can investigate in the coming 10 years. An important learning point of the past 10 years is that data analysis methods often lead to predictions with a certain percentage of false positives. That is often not good enough for users of systems to replace a component or module preventively. But these predictions can still be useful to be better prepared when a failure occurs.

 

Bio
Geert-Jan van Houtum is Professor of Maintenance, Reliability, and Quality at the Department Industrial Engineering and Innovation Sciences (IE&IS) of Eindhoven University of Technology since 2008. Prior to that he filled positions as assistant and associate professor at the same department (1999-2007) and the University of Twente (1994-1998) and as visiting professor at Carnegie Mellon University (2001). He obtained his M.Sc and Ph.D. degree in Applied Mathematics from Eindhoven University of Technology in 1990 and 1995, respectively.

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