Machine Condition Monitoring

Machine condition monitoring aims to predict and detect anomalies in industrial machinery to optimize maintenance and prevent failures. Current research emphasizes developing robust and adaptable models, focusing on deep learning architectures like transformers and convolutional neural networks, often incorporating techniques like self-supervised learning and active learning to address data scarcity and domain shifts. These advancements improve the accuracy and efficiency of fault detection and prediction, leading to significant cost savings and increased safety in industrial settings.