Failure Detection

Failure detection research focuses on reliably identifying malfunctions in diverse systems, from spacecraft to autonomous vehicles, aiming to improve safety and efficiency. Current efforts leverage machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, such as LSTMs), along with ensemble methods and novel approaches like leveraging large language models for improved interpretability and failure explanation. This field is crucial for ensuring the safe and reliable operation of complex systems across various sectors, impacting areas such as aerospace, manufacturing, and healthcare through improved system monitoring and predictive maintenance.