State-of-the-Art Detection and Diagnosis Methods for Rolling Bearing Defects: A Comprehensive Review

• Published in: Applied sciences Vol. 15; no. 2; p. 1001
• Main Authors: Sun, Bojun, Sheng, Zixin, Song, Peng, Sun, Huilin, Wang, Fei, Sun, Xiaogang, Liu, Junyan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2025
• Subjects: 21st century; Artificial intelligence; bearing defect; bearing failure modes; Bearings; Data processing; Deep learning; Deformation; detection and diagnosis methods; Electromagnetism; Failure; Fourier transforms; Friction; Interdisciplinary aspects; Lubricants & lubrication; multimodal measurement; Neural networks; Research methodology; Sensors; Signal processing; Technology; Vibration analysis
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15021001

Rolling bearings are essential transmission and support components in aircraft engines, playing a critical role in ensuring their safe and stable operation. Rolling bearing faults have a significant impact and should not be ignored. The effective diagnosis of bearing faults has always been a critical requirement for ensuring reliable operation. With the increasing demands of modern manufacturing to reduce costs and improve quality, the development of advanced bearing fault detection methods has become indispensable. This paper presents the brief review of recent trends in research on bearing failure modes, bearing fault detection and diagnosis methods, and development trends and prospects. This article provides a comprehensive review of the existing fault diagnosis methods for rolling bearings in four aspects: the integration of advanced sensor technology and advanced data processing technology, multimodal fusion, intelligent detection, and real-time monitoring. It discusses methods based on vibration analysis, acoustic methods, current-based methods, electromagnetic methods, infrared methods, radiographic methods, visual methods, and intelligent detection methods. This study reveals that the application of intelligent detection technology, multimodal fusion detection technology, and real-time online monitoring technology has achieved favorable results. In the future, bearing fault detection will develop in a more intelligent, integrated, and real-time direction.