Data-Centric Analysis of Complex Industrial Systems
Abstract
Modern industrial Cyber-Physical Systems (CPSs) combine distributed computing, software and sensors,
producing rich streams of operational data. This talk explores how these data can support anomaly
detection and identification, using semiconductor photolithography machines as a case study.
Repetitive execution phases provide a basis for describing normal behaviour and identifying
deviations, while selective data collection helps balance useful observation against processing
overhead.
We present behavioural fingerprinting approaches that combine electrical metrics with machine
learning. Phase-based regression models and power passports support traditional classifiers, while
convolutional neural networks offer an alternative with less feature engineering. The talk compares
these approaches in terms of accuracy, preprocessing effort and explainability, and discusses how
the available data and domain knowledge shape the choice of solution. It concludes with challenges
in knowledge incorporation and generalising data-centric methods across systems.
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