A group-aware temporal framework for quality indicator prediction and anomaly detection in production
Thanos Kontogiannis (TU Delft - Aerospace Engineering)
Dimitrios Zarouchas (TU Delft - Aerospace Engineering)
Nick Eleftheroglou (TU Delft - Aerospace Engineering)
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Abstract
This work presents a data analysis-based, group-aware framework for predicting quality indicators with anomaly detection in non-i.i.d. datasets that exhibit short temporal dependencies. The design is motivated by statistical diagnostics of temporal autocorrelation and intraclass variance, which highlight the need for causal temporal encoding and group-level decomposition. The framework integrates a residual-boosted regressor, a group-aware anomaly detector, and a calibrated fusion scheme that balances precision and recall. Evaluation is conducted on real production data from hot strip mill operations, with coiling temperature prediction serving as a case study. A key contribution is interpreting coiling temperature dips, previously treated as outliers, as proxies for surface anomalies, thereby enabling their explicit detection. Results demonstrate consistent gains over physics-based and tabular machine learning baselines, confirming that the framework provides more reliable quality-risk indication for decision support in industrial predictive-control workflows.
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