Automatic Fault Diagnosis Technology of Roller Bearings of High-speed Rail Based on IFD and AE


  • Na Meng The Open University of Shaanxi
  • Sha Li The Open University of Shaanxi
  • Meizhu Li The Open University of Shaanxi
  • Jiang Wei The Open University of Shaanxi
  • Sheng Wang The Open University of Shaanxi



Incipient Failure Detection, Acoustic emission, Roller bearing, Diagnostic technique


INTRODUCTION: With the development of technology and policy support, high-speed rail's temporal and spatial layout is gradually expanding, and it becomes essential to ensure high-safety operation.

OBJECTIVES: The real-time correlation fault diagnosis technology of critical components of electromechanical systems of high-speed trains is analyzed, and a new method of automatic fault diagnosis based on genetic support vector machine is proposed.

METHODS: In this study, the Author combines two techniques, IFD and AE, and introduces an adaptive weighting algorithm to fuse the data of the two and experimentally verify their accuracy.

RESULTS: The experimental results show that in the IFD experiment, the 2-point frequency at 1050 speed is 347.6 Hz, and the 3-point frequency is 498.4 Hz, both of which are very close to the 2 and 3 times frequencies of the 1-point frequency, and the multiplicative relationship is much more straightforward.

CONCLUSION: Combining IFD and AE can realize automatic and accurate diagnosis of bearing state and pre-diagnosis of bearings by adaptive weighted fusion algorithm, which is effective in the practical mechanical diagnosis of rolling bearing faults in high-speed railroads.


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Wang X, Lv Z, Liu W, et al. Application Research on Mechanical Automation Technology in Mechanical Control [J]. International Core Journal of Engineering, 2022, 8(3): 47-50.

Liu Y, Wang B, Yang S, et al. Characteristic analysis of mechanical thermal coupling model for bearing rotor system of high-speed train [J]. Applied Mathematics and Mechanics, 2022, 43(9): 1381-1398.

Li Y. Exploring real-time fault detection of high-speed train traction motor based on machine learning and wavelet analysis [J]. Neural Computing and Applications, 2022, 34(12): 9301-9314.

Li Z, Lv Y, Yuan R, et al. An intelligent fault diagnosis method of rolling bearings via variational mode decomposition and standard spatial pattern-based feature extraction [J]. IEEE Sensors Journal, 2022, 22(15): 15169-15177.

Liu Y Z, Zou Y S, Wu Y, et al. A novel abnormal detection method for bearing temperature based on spatiotemporal fusion[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2022, 236(3): 317-333.

Zhao Y, Zhang J. Recent Patents on Detection of Bearing Temperature[J]. Recent Patents on Engineering, 2022, 16(3): 64-75.

Akhenia P, Bhavsar K, Panchal J, et al. Fault severity classification of ball bearing using SinGAN and deep convolutional neural network [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2022, 236(7): 3864-3877.

Xiao X, Li C, Huang J, et al. Fault Diagnosis of Rolling Bearing Based on Knowledge Graph With Data Accumulation Strategy[J]. IEEE Sensors Journal, 2022, 22(19): 18831-18840.

Wang H, Du W. Early weak fault diagnosis of rolling element bearing based on resonance sparse decomposition and multi-objective information frequency band selection method [J]. Journal of Vibration and Control, 2022, 28(19-20): 2762-2776.

Gong T, Yang J, Liu S, et al. Non-stationary feature extraction by the stochastic response of coupled oscillators and its application in bearing fault diagnosis under variable speed condition [J]. Nonlinear Dynamics, 2022, 108(4): 3839-3857.

Guo J, Shi Z, Zhen D, et al. Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis [J]. Structural Health Monitoring, 2022, 21(3): 984-1011.

Hou D, Qi H, Luo H, et al. Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains [J]. Structural Health Monitoring, 2022, 21(4): 1518-1540.

Aasi A, Tabatabaei R, Aasi E, et al. Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission[J]. Journal of Vibration and Control, 2022, 28(19-20): 2585-2595.

Li Y, Xu F. Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions [J]. Structural Health Monitoring, 2022, 21(4): 1407-1431.

Jawad S M, Jaber A A. Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis [J]. Engineering and Technology Journal, 2022, 41(1): 86-95.

Fu W, Jiang X, Tan C, et al. Rolling Bearing Fault Diagnosis in Limited Data Scenarios Using Feature Enhanced Generative Adversarial Networks[J]. IEEE Sensors Journal, 2022, 22(9): 8749-8759.

Zhen D, Li D, Feng G, et al. Rolling bearing fault diagnosis based on VMD reconstruction and DCS demodulation [J]. International Journal of Hydromechatronics, 2022, 5(3): 205-225.

Barile C, Casavola C, Pappalettera G, et al. Acoustic emission waveforms for damage monitoring in composite materials: shifting in spectral density, entropy and wavelet packet transform [J]. Structural Health Monitoring, 2022, 21(4): 1768-1789.




How to Cite

Meng N, Li S, Li M, Wei J, Wang S. Automatic Fault Diagnosis Technology of Roller Bearings of High-speed Rail Based on IFD and AE. EAI Endorsed Trans Energy Web [Internet]. 2023 Sep. 19 [cited 2023 Oct. 4];10. Available from: