Fault Prediction

Fault prediction research aims to anticipate failures in diverse systems, from automotive engines and power grids to software and athletic performance, improving efficiency and safety. Current efforts leverage machine learning, employing models like transformers, generative adversarial networks, and convolutional kernels to analyze complex datasets and predict fault occurrence, often incorporating real-world operational factors. This field is crucial for predictive maintenance, enhancing system reliability, and optimizing resource allocation across various industries, with ongoing research focused on improving model robustness, accuracy, and handling of concept drift and adversarial attacks.