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
4.1 Feature and data subset selection for contextual anomaly detection using hybrid models
In this project, methods for feature selection and data subset selection will be developed that leverage the advantages of hybrid models, enabling more accurate contextual anomaly detection while making it unnecessary to collect and store all data. The selected features and data will be combined with existing machine learning methods to obtain accurate preventive maintenance, ensuring low maintenance costs and high system availability.
4.2 Probabilistic anomaly detection for online-monitoring/estimation in data-driven resilient control
Please take a look at our vacancy page
4.3 Hybrid model-data approach for machine level anomaly detection and isolation
This project develops techniques for detection and isolation of anomalies. A hybrid approach combining the strengths of both models and data will be pursued. The strength of the model ingredient is that physics-based insight is firmly embedded in the detection strategy warranting the validity of the approach, also in scenarios in which system parameters may change. The strength of using data is twofold: 1) using learning techniques employing measured machine data, the healthy model parameters can be tuned online and/or 2) the design of the detection mechanisms can be tuned online based on data to secure reliable detection.