Verifying Autoencoders for Anomaly Detection in Predictive Maintenance

In recent years, the application of artificial intelligence and machine learning techniques has gained significant traction in addressing various challenges across industries. Among these, anomaly detection has emerged as a crucial task for ensuring the integrity, security, and reliability of complex systems. Autoencoders, a class of neural networks, have shown promising results in capturing intricate patterns and deviations from the norm, making them a popular choice for anomaly detection tasks. However, the robustness and reliability of these autoencoder-based anomaly detection systems in real-life scenarios remain a critical concern. This paper presents an investigation into the verification of autoencoders for anomaly detection applied in a predictive maintenance case study.