Intelligent Fault Detection in HIL Simulations: Leveraging AI and ML for Enhanced Operational Reliability

With the escalating complexity of engineering systems, the demand for reliable fault detection mechanisms in real-time simulations has become imperative. Traditional methods often fall short of capturing intricate patterns and trends, necessitating the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research presents a comprehensive framework for intelligent fault detection in Hardware-in-the-Loop (HIL) simulations, leveraging advanced ML algorithms to analyze data, predict trends, and detect anomalies with precision. The methodology encompasses fault inspection, ML model training, temporal interpolation, event timing alignment, comparative analysis, anomaly tracing, and classification. Extensive experimentation with real-world data validates the efficacy of the proposed framework, demonstrating high accuracy in anomaly detection across various fault types. The framework's adaptability to dynamic environments and its potential for enhancing operational reliability make it an asset in defence, automotive, industrial automation, healthcare, energy, and IoT domains.