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Talk · Asia Conference on Cognitive Engineering and Intelligent Interaction · December 1, 2025

Improving Predictive Maintenance in Bakery Digital Twins: A Hybrid Autoencoder-LSTM Framework with Adaptive Anomaly Thresholding

Liya Erbolkyzy Bektemir, Nazly Yusubova, Bauyrzhan Amirkhanov, Gulshat A. Amirkhanova, Meruyert Sakypbekova

Liya Erbolkyzy Bektemir
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Abstract

The implementation of Digital Twin (DT) systems in food manufacturing is pivotal for realizing Predictive Maintenance (PdM) under Industry 4.0. However, the reliability of such systems is frequently compromised by harsh operational environments that accelerate sensor degradation (drift), leading to high false alarm rates. To address this, we propose a novel Hybrid Autoencoder-LSTM (HAE-LSTM) framework optimized for multivariate IIoT time series. Unlike static thresholding, our proposed Adaptive Anomaly Threshold (AAT) mechanism dynamically modulates detection sensitivity by correlating reconstruction error with a calculated degradation feature vector, effectively decoupling long-term sensor drift from genuine process anomalies. Experimental validation on a real-world dataset from a bakery enterprise demonstrates that the HAE-LSTM achieves an F1-score of 0.92, significantly outperforming baseline Isolation Forest (0.71) and SARIMA (0.66) models. Furthermore, the framework reduces false positive alarms by over 60%, enabling the Digital Twin’s optimization algorithms to secure a 24.4% reduction in production cycle time.

Fault Detection and Control SystemsMachine Fault Diagnosis TechniquesDigital Transformation in Industrypredictive maintenancedigital twinbakery

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