Weak Fault Diagnosis Method of Rolling Bearings Based on Variational Mode Decomposition and a Double-Coupled Duffing Oscillator
Source abstract
Abstract
Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and VMD. The influence rule of system initial value on the response characteristics of a double-coupled Duffing system is studied, and the basis for its determination is given. The frequency of the built-in power of the system is normalized, and a variance evaluation standard for the output value of the double-coupled Duffing system for weak fault signals detection is established. In order to solve the interference problem of fault monitoring signals, VMD is proposed to pre-process the fault monitoring signals. The weak fault signal detection method proposed in this paper is tested and verified by simulation signals and rolling bearing fault signals. The results show that the method proposed in this paper can detect the weak fault signal with the lowest signal-to-noise ratio reduced by 2.96 dB compared with the traditional Duffing detection system, and it can accurately detect the early weak fault signal of rolling bearings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis · Fault Detection and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
School of Equipment Management & Support, Engineering University of PAP, Xi’an 710078, China
References (28)
Show source-provided references
1. A survey and classification of incipient fault diagnosis approaches10.1016/j.jprocont.2020.11.005 ↗
2. Modelling and analysis of a cracked rotor: A review of the literature and its implications10.1007/s00419-020-01667-6 ↗
3. Yan, G.X., Chen, J., Bai, Y., Yu, C.Q., and Yu, C.M. (2022). A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles. Processes, 10.10.3390/pr10040724 ↗
4. Wavelets for fault diagnosis of rotary machines: A review with applications10.1016/j.sigpro.2013.04.015 ↗
5. Analogue circuit fault diagnosis based on convolution neural network10.1049/el.2019.2892 ↗
6. Deep Learning-Based Intelligent Fault Diagnosis Methods toward Rotating Machinery10.1109/ACCESS.2019.2963092 ↗
7. A New Two-Level Hierarchical Diagnosis Network Based on Convolutional Neural Network10.1109/TIM.2019.2896370 ↗
8. Fault diagnosis of neural network classified signal fractal feature based on SVM
9. Research and application of weak fault diagnosis method based on asymmetric potential stochastic resonance10.1177/0020294019830435 ↗
10. An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection10.1109/TLA.2020.9099687 ↗
11. Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis10.1016/j.ymssp.2016.04.002 ↗
12. Weak signal detection based on improved chaotic oscillator system with dual coupling
13. A New Detecting Method for Underwater Acoustic Weak Signal Based on Differential Double Coupling Oscillator10.1109/ACCESS.2021.3052057 ↗
14. Weak Signal Detection Based on Strongly Coupled Duffing-Van der Pol Oscillator and Long Short-term Memory10.7566/JPSJ.89.014003 ↗
15. Detection and parameter estimation of weak pulse signal based on strongly coupled Duffing oscillators10.7498/aps.68.20181856 ↗
16. Weak signal detection based on two-coupled high-order Duffing oscillator system
17. Detecting the State of the Duffing Oscillator by Phase Space Trajectory Autocorrelation10.1142/S021812741350065X ↗
18. A Novel Method for Frequency Feature Extraction of Ship Radiated Noise Based on Variational Mode Decomposition, Double Coupled Duffing Chaotic Oscillator and Multivariate Multiscale Dispersion Entropy10.1016/j.aej.2021.11.059 ↗
19. Fu, W.L., Tan, J.W., Li, C.S., Zou, Z.B., Li, Q.K., and Chen, T. (2018). A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm. Entropy, 20.10.3390/e20090626 ↗
20. Hou, S.Z., and Guo, W. (2022). Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network. Symmetry, 12.10.3390/sym12040536 ↗
21. A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator10.1016/j.chaos.2015.09.007 ↗
22. Liang, T., and Lu, H. (2022). A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing. Entropy, 22.10.3390/e22090995 ↗
23. Birx, D.L., and Pipenberg, S.J. (1992, January 7–11). Chaotic oscillators and complex mapping feed forward networks (CMFFNS) for signal detection in noisy environments. Proceedings of the Proceedings IJCNN International Joint Conference on Neural Networks, Baltimore, MD, USA.
24. A new Duffing detection method for underwater weak target signal10.1016/j.aej.2021.08.016 ↗
25. Statistical approach to weak signal detection and estimation using Duffing chaotic oscillators10.1007/s11432-011-4308-6 ↗
26. Study on partial discharge signal detection by coupled Duffing oscillators10.7498/aps.62.130505 ↗
27. Study on partial discharge signals detection by extened Duffing oscillator10.7498/aps.64.070505 ↗
28. Variational Mode Decomposition10.1109/TSP.2013.2288675 ↗