Rolling Bearing Feature Extraction Method Based on Improved Intrinsic Time-Scale Decomposition and Mathematical Morphological Analysis
Source abstract
Abstract
To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction 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 Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Inner Mongolia Test Institute of Aerospace Dynamic Machine, Hohhot 010076, China
Aero Engine Corporation of China Harbin Bearing Co., LTD, Harbin 150500, China
Undergraduate College, Songsim Global Campus, The Catholic University of Korea, Bucheon-Si 14662, Korea
References (46)
Show source-provided references
1. A fault diagnosis approach for rolling element bearings based on RSGWPT-LCD bilayer screening and extreme learning machine10.1109/ACCESS.2017.2675940 ↗
2. Weak fault feature extraction of rolling bearings using local mean decomposition-based multilayer hybrid denoising10.1109/TIM.2017.2751878 ↗
3. A time varying filter approach for empirical mode decomposition10.1016/j.sigpro.2017.03.019 ↗
4. An adaptive method based on fractional empirical wavelet transform and its application in rotating machinery fault diagnosis10.1088/1361-6501/aaf8e6 ↗
5. SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient10.1088/0957-0233/26/8/085014 ↗
6. Applications of stochastic resonance to machinery fault detection: A review and tutorial10.1016/j.ymssp.2018.12.032 ↗
7. Intelligent fault-prediction assisted self-healing for embryonic hardware10.1109/TBCAS.2020.2995784 ↗
8. Machine learning-based approach for hardware faults prediction10.1109/TCSI.2020.3010743 ↗
9. Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems10.1109/TIE.2018.2873546 ↗
10. A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments
11. Intrinsic time-scale decomposition: Time–frequency–energy analysis and real-time filtering of non-stationary signals
12. Amirat, Y., Choqueuse, V., and Benbouzid, M. (2013). EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component. Mech. Syst. Signal Process.10.1109/IECON.2012.6389263 ↗
13. Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines10.1631/FITEE.1500337 ↗
14. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine10.1007/s12206-017-0107-3 ↗
15. Fault diagnosis for the gearbox of wind turbine combining ensemble intrinsic time-scale decomposition with Wigner bi-spectrum entropy10.21595/jve.2017.17465 ↗
16. Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine10.1177/0954406217691554 ↗
17. Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum10.21595/jve.2017.18616 ↗
18. Sparse coding shrinkage in intrinsic time-scale decomposition for weak fault feature extraction of bearings10.1109/TIM.2018.2801040 ↗
19. Kernel regression residual signal-based improved intrinsic time-scale decomposition for mechanical fault detection10.1088/1361-6501/aaf252 ↗
20. Li, Z., Li, Y., and Zhang, K. (2019). A feature extraction method of ship-radiated noise based on fluctuation-based dispersion entropy and intrinsic time-scale decomposition. Entropy, 21.10.3390/e21070693 ↗
21. Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method10.1177/1461348419867012 ↗
22. EEMD-based notch filter for induction machine bearing faults detection10.1016/j.apacoust.2017.12.030 ↗
23. Selection principle of mathematical morphological operators in vibration signal processing10.1177/1077546314560783 ↗
24. Fault diagnosis of rolling element bearings based on modified morphological method10.1016/j.ymssp.2010.10.008 ↗
25. Average combination difference morphological filters for fault feature extraction of bearing
26. Oppenheim, “Quantum signal processing”10.1109/MSP.2002.1043298 ↗
27. Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox10.1016/j.ymssp.2013.06.035 ↗
28. Application of the Teager–Kaiser energy operator in bearing fault diagnosis10.1016/j.isatra.2012.12.006 ↗
29. A B-spline approach for empirical mode decompositions10.1007/s10444-004-7614-3 ↗
30. Improve the envelope of EMD with piecewise linear fractal interpolation10.4028/www.scientific.net/KEM.439-440.390 ↗
31. Zhu, W., Zhao, H., and Chen, X. (2012, January 19–20). Improving empirical mode decomposition with an optimized piecewise cubic Hermite interpolation method. Proceedings of the 2012 International Conference on Systems and Informatics (ICSAI2012), Yantai, China.10.1109/ICSAI.2012.6223368 ↗
32. Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings10.1109/TIE.2017.2650873 ↗
33. Despeckling of medical ultrasound images based on quantum-inspired adaptive threshold10.1049/el.2010.1092 ↗
34. An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction10.1088/1361-6501/abbc48 ↗
35. Fault diagnosis using pattern classification based on one-dimensional adaptive rank-order morphological filter10.1016/j.jprocont.2011.12.005 ↗
36. Bearing fault signal analysis based on an adaptive multiscale combined morphological filter
37. Multiscale morphology analysis and its application to fault diagnosis10.1016/j.ymssp.2007.09.010 ↗
38. Quantum digital image processing algorithms based on quantum measurement10.1016/j.ijleo.2013.05.063 ↗
39. Kiranyaz, Ç.S., and Gabbouj, M. (2013, January 15–19). Quantum mechanics in computer vision: Automatic object extraction. Proceedings of the 2013 IEEE International Conference on Processing, Melbourne, Australia.
40. Application of morphological operators as envelope extractors for impulsive-type periodic signals10.1006/mssp.2002.1576 ↗
41. Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22–27). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.10.1109/ICASSP.2011.5947265 ↗
42. Modulation Sideband separation using the Teager-Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Moters
43. Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis10.1016/j.ymssp.2019.106545 ↗
44. A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery10.1016/j.ymssp.2017.02.036 ↗
45. A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach10.1016/j.measurement.2008.09.011 ↗
46. Dose-response analyses using restricted cubic spline functions in public health research10.1002/sim.3841 ↗