Towards Detection of Anomalies in Automated Guided Vehicles Based on Telemetry Data

The rapid evolution of smart manufacturing and the pivotal role of Automated Guided Vehicles (AGVs) in enhancing operational efficiency, underscore the necessity for robust anomaly detection mechanisms. This paper presents a comprehensive approach to detecting anomalies based on AGV telemetry data, leveraging the potential of machine learning (ML) algorithms to analyze complex data streams and time series signals. By focusing on the unique challenges posed by real-world AGV environments, we propose a methodology that integrates data collection, preprocessing, and the application of specific AI/ML models to accurately identify deviations from normal operations. Our approach is validated through extensive experiments on datasets featuring anomalies caused by mechanical wear or excessive friction and issues related to tire and wheel damage, employing LSTM and GRU networks, alongside traditional classifiers like K-nearest neighbors and SVM. The results demonstrate the efficacy of our method in forecasting momentary power consumption as an indicator of mechanical anomalies, and in classifying wheel-related issues with high accuracy. This work not only contributes to the enhancement of predictive maintenance strategies but also provides valuable insights for the development of more resilient and efficient AGV systems in smart manufacturing environments.