journal article·2025-01-20Open access

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

Bojun Sun, Zixin Sheng, Peng Song, Huilin Sun, Fei Wang, Xiaogang Sun, Junyan Liu

Applied Sciences· Volume 15· Issue 2

Abstract

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Industrial Vision Systems and Defect Detection · Gear and Bearing Dynamics Analysis · Machine Fault Diagnosis Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Aero Engine Corporation of China Harbin Bearing Company, Ltd., Harbin 150500, China

School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China

Funding

China Aero Engine Group Innovation Fund · Hei longjiang Province Youth Science and Technology Talent Support Project · Technical basic scientific research project

References (100+)

Show source-provided references
1. Bearings for aerospace applications10.1016/j.triboint.2023.108312 ↗
2. A new unsupervised health index estimation method for bearings early fault detection based on Gaussian mixture model10.1016/j.engappai.2023.107562 ↗
3. Bearing damage analysis
4. Tribological aspects of rolling bearing failures
5. Grease degradation in R0F bearing tests10.1080/10402000701261003 ↗
6. Steels for bearings10.1016/j.pmatsci.2011.06.002 ↗
7. A review of bearing failure Modes, mechanisms and causes10.1016/j.engfailanal.2023.107518 ↗
8. Functionally graded structure of a nitride-strengthened Mg2Si-based hybrid composite10.1016/j.jma.2024.03.008 ↗
9. Fault detection analysis in rolling element bearing: A review10.1016/j.matpr.2017.02.054 ↗
10. Diagnosis and root cause analysis of bearing failure using vibration analysis techniques10.1016/j.engfailanal.2023.107954 ↗
11. Bearing fault detection with vibration and acoustic signals: Comparison among different machine leaning classification methods10.1016/j.engfailanal.2022.106515 ↗
12. Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning10.1080/10589759.2022.2118747 ↗
13. An overview of vibration analysis techniques for the fault diagnostics of rolling bearings in machinery
14. Review of condition monitoring of rolling element bearing using vibration analysis and other techniques10.1007/s42417-019-00119-y ↗
15. Bearings health monitoring based on frequency-domain vibration signals analysis10.30684/etj.2022.131581.1043 ↗
16. Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction10.1088/1361-6501/ad5deb ↗
17. Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion10.1016/j.jfranklin.2022.11.004 ↗
18. Dynamic response and failure analysis of bearing under the impact of vibration excitation10.1016/j.engfailanal.2023.107640 ↗
19. Research of fault diagnosis of mine rolling bearing based on time-frequency analysis10.1088/1742-6596/2459/1/012081 ↗
20. Rolling bearing fault diagnosis based on time-frequency feature extraction and IBA-SVM10.1109/ACCESS.2022.3198701 ↗
21. Tang, J., Wu, J., Qing, J., and Kang, T. (2022). Rolling bearing fault monitoring for sparse time-frequency representation and feature detection strategy. Entropy, 24.10.3390/e24121822 ↗
22. Al-Sa’d, M., Jalonen, T., Kiranyaz, S., and Gabbouj, M. (2024). Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults. arXiv.
23. A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed10.1016/j.jsv.2023.117785 ↗
24. Rolling bearing fault diagnosis method using time-frequency information integration and multi-scale TransFusion network10.1016/j.knosys.2023.111344 ↗
25. A time-frequency spectral amplitude modulation method and its applications in rolling bearing fault diagnosis10.1016/j.ymssp.2022.109832 ↗
26. A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings10.1016/j.ymssp.2021.108616 ↗
27. Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique10.1088/1361-6501/acabdb ↗
28. Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation10.1016/j.ymssp.2024.111823 ↗
29. Time-frequency supervised contrastive learning via pseudo-labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds10.1016/j.aei.2023.102304 ↗
30. Advanced bearing diagnostics: A comparative study of two powerful approaches10.1016/j.ymssp.2018.05.011 ↗
31. Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis10.1016/j.matpr.2021.05.447 ↗
32. Feature enhancement method of rolling bearing acoustic signal based on RLS-RSSD10.1016/j.measurement.2022.110883 ↗
33. Ultrasonic measurement of roller revolution speed for high-speed rolling bearing10.1016/j.measurement.2023.113641 ↗
34. Clarke, B.P. (2022). Development of Ultrasonic Techniques for Rolling Element Bearing Monitoring. [Ph.D. Thesis, University of Sheffield].
35. Modeling and effect of ultrasonic waves on bearing shells in industry by non-destructive testing10.1134/S1061830920100058 ↗
36. Moshrefi, A., and Nabki, F. (2024). Advanced industrial fault detection: A comparative analysis of ultrasonic signal processing and ensemble machine learning techniques. Appl. Sci., 14.10.3390/app14156397 ↗
37. Rolling bearing fault diagnosis based on improved GAN and 2-D representation of acoustic emission signals10.1109/ACCESS.2022.3193244 ↗
38. Rolling bearing condition monitoring technique based on cage rotation analysis and acoustic emission
39. Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings10.1177/14759217231164912 ↗
40. High-speed train wheel set bearing fault diagnosis and prognostics: Research on acoustic emission detection mechanism10.1016/j.ymssp.2022.109325 ↗
41. Scheeren, B., Kaminski, M.L., and Pahlavan, L. (2022). Evaluation of ultrasonic stress wave transmission in cylindrical roller bearings for acoustic emission condition monitoring. Sensors, 22.10.3390/s22041500 ↗
42. New evaluation method for defects propagation in toroidal roller bearings using dominant frequency of acoustic emission10.1080/10589759.2024.2402885 ↗
43. Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission10.1177/10775463211016130 ↗
44. Online diagnosis of small faults in rolling bearings using FBG acoustic emission sensors inside the accelerated life tester10.1109/JSEN.2022.3230892 ↗
45. Enhancing bearing fault diagnosis using motor current signals: A novel approach combining time shifting and CausalConvNets10.1016/j.measurement.2023.114049 ↗
46. Computational intelligence to detect bearing faults using optimal features from motor current signals10.1080/21642583.2024.2437157 ↗
47. A zoomed root-Prony technique for efficient bearing fault detection in induction motors10.1016/j.rineng.2024.103367 ↗
48. A current signal-based adaptive semisupervised framework for bearing faults diagnosis in drivetrains
49. Barcelos, A.S., and Cardoso, A.J.M. (2021). Current-based bearing fault diagnosis using deep learning algorithms. Energies, 14.10.3390/en14092509 ↗
50. Wang, Z., Guan, C., Shi, S., Zhang, G., and Gu, X. (2024). Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention. World Electr. Veh. J., 15.10.3390/wevj15070281 ↗
51. Kobayashi, D., Ono, K., Natori, M., and Komata, H. (2023, January 11–14). Non-destructive Prognostics for Rolling Bearings by Eddy Current Testing. Proceedings of the 4th Asia-Pacific Conference of the Prognostics and Health Management, Tokyo, Japan.10.36001/phmap.2023.v4i1.3715 ↗
52. A new micro magnetic bridge probe in magnetic flux leakage for detecting micro-cracks10.1007/s10921-018-0499-8 ↗
53. Guo, Y., Hu, Y., Wang, K., Song, Y., Feng, B., Kang, Y., and Duan, Z. (2024). Bottom Crack Detection with Real-Time Signal Amplitude Correction Using EMAT-PEC Composite Sensor. Sensors, 24.10.20944/preprints202407.2152.v1 ↗
54. Dong, Y., Ma, Y., Qiu, M., Chen, F., and He, K. (2024). Analysis and experimental research of transient temperature rise characteristics of high-speed cylindrical roller bearing. Sci. Rep., 14.10.1038/s41598-024-51255-9 ↗
55. Thermovision measurements of rolling bearings10.1016/j.measurement.2021.110512 ↗
56. Infrared thermography-based fault diagnosis of induction motor bearings using machine learning10.1109/JSEN.2020.3015868 ↗
57. Alvarado-Hernandez, A.I., Zamudio-Ramirez, I., Jaen-Cuellar, A.Y., Osornio-Rios, R.A., Donderis-Quiles, V., and Antonino-Daviu, J.A. (2022). Infrared thermography smart sensor for the condition monitoring of gearbox and bearings faults in induction motors. Sensors, 22.10.3390/s22166075 ↗
58. Zhou, J., Liu, L., Shen, X., and Yang, X. (2023). RFR-GA-BLS: A feature selection and parameter optimization method for fault diagnosis of rolling bearing using infrared images. Appl. Sci., 13.10.3390/app13137350 ↗
59. Unsupervised Feature-Preserving CycleGAN for Fault Diagnosis of Rolling Bearings Using Unbalanced Infrared Thermal Imaging Sample10.1109/ACCESS.2024.3365551 ↗
60. X-ray computed tomography10.1038/s43586-021-00015-4 ↗
61. X-ray Tomography in The Diagnostics of Roller Bearing Rings10.4028/www.scientific.net/SSP.223.211 ↗
62. An analysis of premature cracking associated with microstructural alterations in an AISI 52100 failed wind turbine bearing using X-ray tomography10.1016/j.matdes.2016.12.089 ↗
63. X-ray CT imaging of grease behavior in ball bearing and numerical validation of multi-phase flows simulation10.2474/trol.15.36 ↗
64. Diagnosis of deterioration of lithium greases used in rolling-element bearings by X-ray diffractrometry10.1080/10402008908981880 ↗
65. High resolution gamma ray tomography scanner for flow measurement and non-destructive testing applications10.1063/1.2795648 ↗
66. Bearing defect inspection based on machine vision10.1016/j.measurement.2011.12.018 ↗
67. State of the art in defect detection based on machine vision10.1007/s40684-021-00343-6 ↗
68. Lu, M., and Chen, C.L. (2021). Detection and classification of bearing surface defects based on machine vision. Appl. Sci., 11.10.3390/app11041825 ↗
69. Machine vision-based surface defect detection method for rolling bearings of metro vehicle
70. Toma, R.N., Prosvirin, A.E., and Kim, J.M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20.10.3390/s20071884 ↗
71. Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression10.1109/TIM.2014.2330494 ↗
72. Insights into modern machine learning approaches for bearing fault classification: A systematic literature review10.1016/j.rineng.2024.102700 ↗
73. Zhou, J., Xiao, M., Niu, Y., and Ji, G. (2022). Rolling bearing fault diagnosis based on WGWOA-VMD-SVM. Sensors, 22.10.3390/s22166281 ↗
74. Deep learning based approach for bearing fault diagnosis10.1109/TIA.2017.2661250 ↗
75. A new convolutional neural network-based data-driven fault diagnosis method10.1109/TIE.2017.2774777 ↗
76. A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine10.1016/j.neucom.2019.03.084 ↗
77. Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis10.1088/1361-6501/acb377 ↗
78. Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework10.1016/j.aei.2022.101552 ↗
79. Image deep learning in fault diagnosis of mechanical equipment10.1007/s10845-023-02176-3 ↗
80. Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method10.1016/j.measurement.2021.110500 ↗
81. A survey on deep learning based bearing fault diagnosis10.1016/j.neucom.2018.06.078 ↗
82. Deep learning algorithms for bearing fault diagnostics—A comprehensive review10.1109/ACCESS.2020.2972859 ↗
83. Wu, G., Ji, X., Yang, G., Jia, Y., and Cao, C. (2023). Signal-to-image: Rolling bearing fault diagnosis using ResNet family deep-learning models. Processes, 11.10.3390/pr11051527 ↗
84. A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations10.1016/j.asej.2022.101945 ↗
85. A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis10.1016/j.ress.2024.110556 ↗
86. Thuan, N.D., and Hong, H.S. (2023). HUST bearing: A practical dataset for ball bearing fault diagnosis. BMC Res. Notes, 16.10.1186/s13104-023-06400-4 ↗
87. An adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis10.1016/j.measurement.2024.114191 ↗
88. A hierarchical attention detector for bearing surface defect detection10.1016/j.eswa.2023.122365 ↗
89. Fault diagnosis of rolling bearing based on WHVG and GCN
90. Novel convolutional neural network (NCNN) for the diagnosis of bearing defects in rotary machinery10.1109/TIM.2021.3055802 ↗
91. Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise10.1016/j.aei.2024.102559 ↗
92. Data-driven intelligent condition adaptation of feature extraction for bearing fault detection using deep responsible active learning10.1109/ACCESS.2024.3380438 ↗
93. Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit10.1016/j.ress.2023.109753 ↗
94. Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis10.1016/j.measurement.2020.108655 ↗
95. Fault diagnosis of rolling bearing based on multimodal data fusion and deep belief network10.1177/09544062211008464 ↗
96. WCNN-RSN: A novel fault diagnosis method for rolling bearing using multimodal feature fusion10.1088/1361-6501/ad7e48 ↗
97. Xu, Z., Chen, X., Li, Y., and Xu, J. (2024). Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis. Sensors, 24.10.3390/s24061792 ↗
98. MgNet: A fault diagnosis approach for multi-bearing system based on auxiliary bearing and multi-granularity information fusion10.1016/j.ymssp.2023.110253 ↗
99. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network10.1016/j.measurement.2020.108518 ↗
100. 2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing10.1016/j.ress.2021.108017 ↗