Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution
Source abstract
Abstract
Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and a challenge is how to accurately separate the inner and outer race fault features from noisy compound faults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Q-factors and improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, the compound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonance components of the signal (compound fault impact component and small amount of noise) are obtained, but it can only highlight the impact of compound faults, and failed to separate the inner and outer race compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection of parameters (the shift order M and the filter length L) based on the iterative calculation method with the Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filtered and de-noised by the proposed method, the inner and outer race fault signals are obtained respectively. The fault characteristic frequency is consistent with the theoretical calculation value. The results show that the proposed method can efficiently separate the mixed fault information and avoid the mutual interference between the components of the compound fault.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District Beijing 100124, China
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Funding
National Natural Science Foundation of China
References (46)
Show source-provided references
1. Step-by-step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory10.1109/TFUZZ.2018.2833820 ↗
2. An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder10.1016/j.engappai.2018.09.010 ↗
3. A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing10.1016/j.measurement.2018.12.028 ↗
4. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery10.1177/0142331217708242 ↗
5. Li, C., de Oliveira, J.L.V., Lozada, M.C., Cabrera, D., Sanchez, V., and Zurita, G. (2019). A systematic review of fuzzy formalisms for bearing fault diagnosis. IEEE Trans. Fuzzy Syst.10.1109/TFUZZ.2018.2878200 ↗
6. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals10.1016/j.compind.2018.12.013 ↗
7. Bearing performance degradation assessment using long short-term memory recurrent network10.1016/j.compind.2018.12.016 ↗
8. Matching Pursuit of an Adaptive Impulse Dictionary for Bearing Fault Diagnosis10.1016/j.jsv.2013.12.029 ↗
9. Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary
10. Application of pattern recognition in gear faults based on the matching pursuit of a characteristic waveform10.1016/j.measurement.2017.03.021 ↗
11. Wang, H.Q., Ren, B.Y., Song, L.Y., and Cui, L.L. (2019). A Novel Weighted Sparse Representation Classification Strategy based on Dictionary Learning for Rotating Machinery. IEEE Trans. Instrum. Meas.10.1109/TIM.2019.2906334 ↗
12. Quantitative trend fault diagnosis of a rolling bearing based on Sparsogram and Lempel-Ziv10.1016/j.measurement.2018.06.051 ↗
13. Analysis of varying contact angles and load distributions in defective angular contact ball bearing10.1016/j.engfailanal.2018.04.050 ↗
14. Cui, L., Jin, Z., Huang, J.F., and Wang, H.Q. Fault severity classification and size estimation for ball bearings based on vibration mechanism. IEEE Access, 2019.10.1109/ACCESS.2019.2911323 ↗
15. Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery10.1109/TIM.2018.2806984 ↗
16. HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault
17. Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet
18. Ulutagay and José Valente de Oliveira, A comparison of fuzzy clustering algorithms for bearing fault diagnosis
19. Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault10.1007/s10409-015-0529-z ↗
20. A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator10.1016/j.chaos.2015.09.007 ↗
21. Compound feature selection and parameter optimization of elm for fault diagnosis of rolling element bearings10.1016/j.isatra.2016.08.022 ↗
22. A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis10.1016/j.measurement.2018.10.098 ↗
23. Tang, G., Luo, G., Zhang, W., Yang, C., and Wang, H. (2016). Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals. Sensors.10.3390/s16060897 ↗
24. Underdetermined Source Separation of Bearing Faults Based on Optimized Intrinsic Characteristic-Scale Decomposition and Local Non-Negative Matrix Factorization10.1109/ACCESS.2019.2892559 ↗
25. Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis10.1109/ACCESS.2018.2886343 ↗
26. Hao, Y., Song, L., Ke, Y., Wang, H., and Chen, P. (2017). Diagnosis of Compound Fault Using Sparsity Promoted-Based Sparse Component Analysis. Sensors, 17.10.3390/s17061307 ↗
27. Blind separation of vibration components: Principles and demonstrations
28. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection
29. Sun, P., Liao, Y., and Lin, J. (2017). The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings. Sensors, 17.10.20944/preprints201701.0091.v1 ↗
30. A time domain approach to diagnose gearbox fault based on measured vibration signals10.1016/j.jsv.2013.11.033 ↗
31. Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution10.3390/s151129363 ↗
32. A Novel Feature Enhancement Method based on Improved Constraint Model of Online Dictionary Learning10.1109/ACCESS.2019.2895776 ↗
33. Initial center frequency-guided VMD for fault diagnosis of rotating machines10.1016/j.jsv.2018.07.039 ↗
34. Improved Fault Size Estimation Method for Rolling Element Bearings Based on Concatenation Dictionary10.1109/ACCESS.2019.2899036 ↗
35. Wavelet Transform with Tunable Q-Factor10.1109/TSP.2011.2143711 ↗
36. Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox
37. Sparsity-based signal extraction using dual Q-factors for gearbox fault detection10.1016/j.isatra.2017.09.018 ↗
38. Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing10.1016/j.measurement.2018.07.043 ↗
39. Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery
40. Morphological component analysis: An adaptive thresholding strategy10.1109/TIP.2007.907073 ↗
41. Diagnosis of rolling element bearing fault a rising in gearbox based on sparse morphological component analysis10.21595/jve.2016.17265 ↗
42. Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes10.1088/0957-0233/26/11/115003 ↗
43. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings
44. A hybrid method combining Teager Kaiser energy operator, empirical mode decomposition and minimum entropy deconvolution for monitoring gears damages
45. On amplitude and frequency demodulation using energy operators10.1109/78.212729 ↗
46. Wan, S.T., Zhou, X., and Dou, L.J. (2018). Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK. Math. Probl. Eng.10.1155/2018/6513045 ↗