Sen, MehmetMutluer, Mumtaz2026-01-102026-01-1020251996-1073https://doi.org/10.3390/en18246402https://hdl.handle.net/123456789/12891Brushless DC (BLDC) motors are widely used in many engineering fields such as transportation, industrial automation, pumping systems, household devices, and renewable energy applications. Their popularity mainly arises from advantages like high power density, low noise, long service life, and high efficiency. This study contributes to the literature by comprehensively addressing the types, applications, faults, and diagnostic methods of BLDC motors. This review systematically examines recent studies to identify and classify common mechanical, electrical, magnetic, thermal, and sensor-related faults. Diagnostic approaches reported in these studies are then analyzed and compared. The methods are grouped into several categories, including signal processing, model-based, data driven, artificial intelligence-supported, and thermal or magnetic monitoring techniques. The review results show that hybrid and intelligent diagnostic strategies, which combine different analysis methods, significantly improve the accuracy of fault detection and enable earlier fault identification. These improvements also contribute to higher reliability and safer operation of BLDC systems. In the discussion, attention is given to the growing use of artificial intelligence and data fusion in fault diagnosis. These trends are likely to guide the next generation of condition monitoring systems for BLDC motors. Overall, this study emphasizes the importance of developing reliable and sustainable diagnostic frameworks to enhance energy efficiency and system performance. The results can provide a useful reference for researchers and engineers working on BLDC motor technologies.eninfo:eu-repo/semantics/openAccessBrushless DC MotorFault DiagnosisHybrid MethodsTypes of FaultsPredictive MaintenanceArticle10.3390/en182464022-s2.0-105026130484