Real-Time Anomaly Detection with LSTM-Autoencoder Network on Microcontrollers for Industrial Applications

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Md Nur Amin, Lea Hubner, Fatih S. Bayram, Alexander Jesser

Published: 2024, Last Modified: 09 Nov 2025ICGSP 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In the fast-paced landscape of modern industry, traditional safety systems struggle to identify and mitigate complex, persistent threats that can lead to operational disruptions and safety hazard. This study uses an anomaly detection system for the BDB 825 diamond dry drilling machine, leveraging a 6-axis accelerometer sensor for comprehensive operational monitoring. The system utilizing Long Short-Term Memory (LSTM) networks and autoencoders, optimized for Arduino microcontrollers using quantization techniques and integrated with TensorFlow Lite for real-time implementation. The effectiveness of this system is demonstrated in its ability to accurately detect anomalies, enhancing operational safety and reducing the risk of disruptions in industrial settings.