Condition Monitoring

Condition monitoring aims to assess the health and predict the remaining useful life of industrial assets, enabling proactive maintenance and preventing costly failures. Current research emphasizes the development of robust and adaptable models, employing techniques like deep learning (including convolutional and recurrent neural networks, autoencoders, and generative adversarial networks), statistical process control, and physics-informed machine learning to handle diverse data types (e.g., vibration, acoustic emission, imagery, tabular data) and address challenges like data scarcity, noise, and heterogeneous temporal dynamics. These advancements are crucial for improving industrial efficiency, safety, and sustainability across various sectors, from manufacturing and energy to transportation and infrastructure.