VARADE++: An Edge-Friendly Framework for Real-Time Anomaly Detection in Production Plants

Abstract

Real-time anomaly detection is pivotal to the success of smart robotics, particularly in production plants, where even minor system failures can result in significant machine downtime and costly process disruptions. To address this, a specialized Machine Learning (ML) model must be seamlessly integrated into a network of interconnected machinery, sensors, and actuators, all processing vast streams of multidimensional sensor data with minimal latency that cloud-based solutions often struggle to achieve. In this work, we introduce VARADE++, an edge-optimized anomaly detection framework designed to navigate the complex trade-offs between anomaly detection accuracy, inference speed, and computational efficiency. By leveraging a lightweight auto-regressive architecture rooted in attentionless transformers, paired with a variational training paradigm, we achieve real-time processing capabilities. Our model is integrated into an advanced IoT infrastructure, enabling low-latency handling of intricate data streams. The effectiveness of VARADE++ is demonstrated across two public benchmarks and validated through a real-world case study within a sensorized industrial pilot production line, with an industrial robot as the primary focus. Our results not only highlight the superior anomaly detection capabilities of VARADE++ but also showcase its operational efficiency in a real-time edge computing environment, outperforming state-of-the-art solutions in the balance between detection performance and practical deployability.