Aangenent, W.H.T.M.

Classens, K.H.J.

Heemels, W.P.M.H. (Maurice)

Oomen, Tom A.E.

van de Wal, Marc M.J.

van de Wijdeven, J.J.M.

English

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 Digital Twins in ControlCitation for published version (APA):Classens, K. H. J., Oomen, T. A. E., Heemels, W. P. M. H., van de Wijdeven, J. J. M., van de Wal, M. M. J., &Aangenent, W. H. T. M. (2020). Digital Twins in Control: From Fault Detection to Predictive Maintenance inPrecision Mechatronics. Abstract from 1st Euspen Special Interest Group Meeting on Precision Motion Systems& Control, Aachen, North Rhine-Westphalia, Germany. https://www.euspen.eu/knowledge-base/PMC20128.pdfDocument status and date:Published: 18/11/2020Document Version:Accepted manuscript including changes made at the peer-review stagePlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 04. Oct. 2023*This work is supported by TKI and ASML Research Digital Twins in Control: From Fault Detection to Predictive Maintenance in Precision Mechatronics* Koen Classens1, Tom Oomen1, W.P.M.H. (Maurice) Heemels1, Jeroen van de Wijdeven2, Marc van de Wal2, Wouter Aangenent2 1Eindhoven University of Technology, The Netherlands 2ASML Research Mechatronics, The Netherlands K.H.J.Classens@tue.nl Abstract The economic value of high-tech production equipment is to a large extent determined by its productivity. In order to maximize productivity, it is essential to minimize unscheduled downtime. Equipment downtime can be minimized by means of predictive maintenance, which can be pursued via the process of predicting and detecting faults and simultaneously pinpointing its origin, which is called Fault Detection and Isolation (FDI).  Traditional FDI systems are either data-driven or based on physical models. As next generation FDI system, a physics-based model is envisaged that is learned from data to improve the predictive capability of the digital counterpart. Models of the system are often available prior to commissioning a machine, for instance, through Finite Element Modeling or identified models during system integration. After system integration and control design, the model is generally left unused.  Evidently, this model is valuable and can be exploited in the form of a digital twin that is informed with real-time data through a large number of sensors and actuators. Comparison of this digital counterpart to the physical plant allows to monitor the ageing of critical components and allows to detect anomalies at an early stage. The underlying physics enable to isolate the origin of anomalous behaviour, which in turn allows for effective self-healing or specific hardware maintenance. The envisioned approach involving data-enriched physics-based digital twins is general in nature and therefore likely to be applicable to a large range of systems, including production machines and scientific instruments. The proof-of concept will demonstrate that the envisioned approach can be applied to a broad industrial range of mechatronic systems far beyond wafer scanners. In this way, the present research project (2020-2024) focuses on bridging the gap between data-based and model-based FDI approaches, which are currently largely separated.  *This work is supported by TKI and ASML Research           Figure 1: Two experimental setups to illustrate predictive maintenance via digital twinning. (a) Free floating reticle stage. (b) Overactuated test rig. 

Digital Twins in Control: From Fault Detection to Predictive Maintenance in Precision Mechatronics

https://pure.tue.nl/ws/files/166149200/Classens_et_al._2020_Digital_Twins_in_Control_From_Fault_Detection_to.pdf

Digital Twins in Control: From Fault Detection to Predictive Maintenance in Precision Mechatronics

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