Data Driven Fault

Data-driven fault diagnosis leverages machine learning to identify and predict malfunctions in diverse systems, aiming to improve safety, efficiency, and maintenance. Current research emphasizes addressing challenges like data scarcity, domain shifts (where models trained on one dataset perform poorly on another), and imbalanced datasets, employing techniques such as unsupervised learning (e.g., clustering algorithms like OPTICS), generative models (e.g., restricted Boltzmann machines), and domain adaptation methods (e.g., maximum mean discrepancy). These advancements are crucial for enhancing the reliability and robustness of fault diagnosis across various applications, from industrial machinery and smart grids to robotics and aerospace systems.