Machinery Fault

Machinery fault diagnosis aims to identify and classify malfunctions in industrial equipment to prevent failures and optimize maintenance. Current research heavily emphasizes data-driven approaches, employing deep learning architectures like convolutional neural networks (CNNs), transformer networks, and Hamiltonian neural networks, often incorporating transfer learning and active learning techniques to improve efficiency and robustness. These advancements leverage diverse sensor data (vibration, acoustic, etc.) and focus on developing more interpretable models while addressing challenges like noisy environments and data scarcity. Improved fault detection leads to enhanced safety, reduced downtime, and cost savings across various industries.