Anomaly Recognition with Trustworthy Neural Networks: a Case Study in Elevator Control

In the realm of contemporary industrial control systems, the necessity for robust anomaly detection and classification is of critical importance. This paper presents an application of neural network technology in a real-world industrial scenario focused on elevator control. We employ two fully-connected neural networks to accomplish both anomaly detection and classification. The first neural network is dedicated to identifying types of anomalies, while the second predicts their magnitudes. Additionally, we integrate formal verification to certify the local robustness of these networks. Our findings not only showcase the practical efficacy of our methodology but also emphasise the crucial role of small neural networks in effectively addressing challenges within industrial settings.