Automatic Fault Diagnosis Technology of Roller Bearings of High-speed Rail Based on IFD and AE
DOI:
https://doi.org/10.4108/ew.3908Keywords:
Incipient Failure Detection, Acoustic emission, Roller bearing, Diagnostic techniqueAbstract
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.
Downloads
References
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. DOI: https://doi.org/10.1007/s10483-022-2893-5
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. DOI: https://doi.org/10.1007/s00521-021-06284-0
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. DOI: https://doi.org/10.1109/JSEN.2022.3184713
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. DOI: https://doi.org/10.1177/09544097211022105
Zhao Y, Zhang J. Recent Patents on Detection of Bearing Temperature[J]. Recent Patents on Engineering, 2022, 16(3): 64-75. DOI: https://doi.org/10.2174/1872212115666210810114300
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. DOI: https://doi.org/10.1177/09544062211043132
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. DOI: https://doi.org/10.1109/JSEN.2022.3201839
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. DOI: https://doi.org/10.1177/10775463211020205
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. DOI: https://doi.org/10.1007/s11071-022-07373-y
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. DOI: https://doi.org/10.1177/14759217211018281
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. DOI: https://doi.org/10.1177/14759217211036025
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. DOI: https://doi.org/10.1177/10775463211016130
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. DOI: https://doi.org/10.1177/14759217211033627
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. DOI: https://doi.org/10.30684/etj.2022.131581.1043
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. DOI: https://doi.org/10.1109/JSEN.2022.3160762
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. DOI: https://doi.org/10.1504/IJHM.2022.125092
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. DOI: https://doi.org/10.1177/14759217211044692
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 EAI Endorsed Transactions on Energy Web
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
