Optimizing Bearing Fault Detection: CNN-LSTM with Attentive TabNet for Electric Motor Systems
Alaa Uthman Khawaja, Ahmad Shaf, Faisal Al Thobiani, Tariq Ali, Muhammad Irfan, Aqib Rehman Pirzada, Unza Shahkeel
Abstract
Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This s... | Find, read and cite all the research you need on Tech Science Press
Topics & Concepts
Bearing (navigation)Fault detection and isolationComputer scienceFault (geology)Artificial intelligencePattern recognition (psychology)ActuatorSeismologyGeologyMachine Fault Diagnosis TechniquesEngineering Diagnostics and Reliability