Technology Health Management
The reliable functioning of the future’s high-tech systems and manufacturing processes requires novel techniques for online assessment of the health state of such systems.
Using the improved digital twins developed in Projects 1, 2 and 3, this project aims at developing methods and tools for pro-active and automated health management of manufacturing processes and (high-tech) machines. Here, health management relates to the safeguarding of system performance and system availability through anomaly detection, preventive maintenance and control reconfiguration.
In this project, we will tackle the following scientific challenges:
- To develop methods for technology health management based on synergy between physical models and data-driven approaches.
- To develop novel solutions for anomaly detection and identification, that are directly useful in solutions for decision-making for preventive maintenance and control reconfiguration.
The research in this project will be valorized through application to high-end printers in collaboration with Canon Production Printing and to wafer handler systems for lithography machines in collaboration with VDL-ETG and ASML.
People on Project 4
Project leader: Prof. dr. ir. Nathan van de Wouw
- Dr. ir. C. Murguia, Eindhoven University of Eindhoven
- Prof. dr. B. Jayawardhana, University of Groningen
- Dr. ir. T. Keviczky, Delft University of Technology
- Dr. P. Mohajerin Esfahani, Delft University of Technology
- Dr. M. van Leeuwen, Leiden University
- Prof. dr. ir. J. Post, Philips B.V. & University of Groningen
4.4 Integration and implementation of hybrid model-data approaches to anomaly detection and control reconfiguration
Anomalies can only be detected with confidence if the anomalous system behaves substantially different than expected. Moreover, for many types of anomalies, this difference is not perceived in normal operation until it is too late; i.e., the control performance has already degraded to the point that the system must stop for maintenance. This is why in many applications there are diagnostic routines, where, for a short moment, the system input is designed to make the system behave in a way that anomalies are more evidently discerned. Project 4.4 investigates the problem of optimally designing such input commands for anomaly detection and estimation. This way, the hybrid model-data tools developed in Project 4 can be used at their full potential, and the control reconfiguration / optimization approaches developed in Project 5 can be employed before it is too late. An emphasis is given to the Tata Steel benchmark problem.