Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures
Source abstract
Abstract
Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods.
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
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), P°. J. M^{a}. Arizmendiarrieta 2, 20500 Arrasate-Mondragón, Spain
Control, Modeling, Identification, and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany 16, 08019 Barcelona, Spain
Funding
Ministerio de Economía, Industria y Competitividad, Gobierno de España · Generalitat de Catalunya
References (68)
Show source-provided references
1. Komusanac, I., Fraile, D., and Brindley, G. (2019). Wind Energy in Europe in 2018, Wind Europe. Technical Report.
2. Walsh, C., Ramírez, L., Fraile, D., and Brindley, G. (2020). Wind Energy in Europe in 2019, Wind Europe. Technical Report.
3. IRENA (2019). Future of Wind: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects (A Global Energy Transformation Paper), International Renewable Energy Agency. Technical Report.
4. IRENA (2012). Renewable Energy Cost Analysis—Wind Power, International Renewable Energy Agency. Technical Report.
5. IRENA (2016). Floating Foundations: A Game Changer for Offshore Wind Power, International Renewable Energy Agency. Technical Report.
6. Lynn, P. (2011). Onshore and Offshore Wind Energy: An Introduction, John Wiley & Sons Ltd.. [1st ed.].10.1002/9781119954613 ↗
7. Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges10.3390/en7042595 ↗
8. A Catalogue Capacity of Slewing Bearings10.1016/j.mechmachtheory.2012.07.012 ↗
9. A Markovian Approach for Modelling the Effects of Maintenance on Downtime and Failure Risk of Wind Turbine Components10.1016/j.renene.2016.05.022 ↗
10. Stamboliska, Z., Rusiński, E., and Moczko, P. (2015). Condition Monitoring Considerations for Low-Speed Machines. Proactive Condition Monitoring of Low-Speed Machines, Springer International Publishing.10.1007/978-3-319-10494-2 ↗
11. Condition Monitoring and Fault Diagnosis Methods for Low-Speed and Heavy-Load Slewing Bearings: A Literature Review10.21595/jve.2017.18454 ↗
12. A Review of Failure Modes, Condition Monitoring and Fault Diagnosis Methods for Large-Scale Wind Turbine Bearings10.1016/j.measurement.2019.107002 ↗
13. Revisión de las técnicas de monitorización del estado de los rodamientos de las palas de los aerogeneradores
14. Sandoval, D., Leturiondo, U., Pozo, F., Vidal, Y., and Salgado, O. (2019, January 25–27). Trends in Condition Monitoring for Pitch Bearings. Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, CM, Glasgow, UK.
15. Monitoring the Onset and Propagation of Natural Degradation Process in a Slow Speed Rolling Element Bearing With Acoustic Emission10.1115/1.2948413 ↗
16. Low Speed Rolling Bearing Diagnostics Using Acoustic Emission and Higher Order Statistics Techniques
17. Defect Diagnosis for Rolling Element Bearings Using Acoustic Emission10.1115/1.4000480 ↗
18. Accelerated Natural Fault Diagnosis in Slow Speed Bearings with Acoustic Emission10.1016/j.engfracmech.2009.09.016 ↗
19. Approximate Entropy Analysis of the Acoustic Emission From Defects in Rolling Element Bearings10.1115/1.4007240 ↗
20. Acoustic Emission-Based Condition Monitoring Methods: Review and Application for Low Speed Slew Bearing10.1016/j.ymssp.2015.10.020 ↗
21. Caesarendra, W., Kosasih, B., Tieu, K., and Moodie, C. (2013, January 9–12). An Application of Nonlinear Feature Extraction—A Case Study for Low Speed Slewing Bearing Condition Monitoring and Prognosis. Proceedings of the 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM, Wollongong, NSW, Australia.10.1109/AIM.2013.6584344 ↗
22. Rojas, A., and Nandi, A.K. (2005, January 28–28). Detection and Classification of Rolling-Element Bearing Faults Using Support Vector Machines. Proceedings of the 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT, USA.
23. Caesarendra, W., and Tjahjowidodo, T. (2017). A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. Machines, 5.10.3390/machines5040021 ↗
24. Moodie, C. (2009). An Investigation into the Condition Monitoring of Large Slow Speed Slew Bearings. University of Wollongong Thesis Collection 1954–2016: Wollongong, University of Wollongong.
25. Caesarendra, W., Tjahjowidodo, T., Kosasih, B., and Tieu, A. (2017). Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings. Machines, 5.10.3390/machines5020011 ↗
26. Shen, G., Wu, Z., and Zhang, J. (2017). Fault Analysis for Low-Speed Heavy-Duty Crane Slewing Bearing Based on Wavelet Energy Spectrum Coefficient. Advances in Acoustic Emission Technology, Springer International Publishing.10.1007/978-3-319-29052-2 ↗
27. Rolling Element Bearing Fault Diagnosis under Slow Speed Operation Using Wavelet De-Noising10.1016/j.measurement.2017.02.033 ↗
28. Vibration Analysis for Large-Scale Wind Turbine Blade Bearing Fault Detection with an Empirical Wavelet Thresholding Method10.1016/j.renene.2019.06.094 ↗
29. An Adaptive Sensitive Frequency Band Selection Method for Empirical Wavelet Transform and Its Application in Bearing Fault Diagnosis10.1016/j.measurement.2018.10.086 ↗
30. Early Diagnosis of Bearing Faults Using Decomposition and Reconstruction Stochastic Resonance System10.1016/j.measurement.2020.107709 ↗
31. Condition Monitoring of Naturally Damaged Slow Speed Slewing Bearing Based on Ensemble Empirical Mode Decomposition10.1007/s12206-013-0608-7 ↗
32. Remaining Useful Life Assessment of Slewing Bearing Based on Spatial-Temporal Sequence10.1109/ACCESS.2020.2965285 ↗
33. Fault Feature Extraction of Low Speed Roller Bearing Based on Teager Energy Operator and CEEMD10.1016/j.measurement.2019.02.053 ↗
34. Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy10.1152/ajpheart.2000.278.6.H2039 ↗
35. Henry, M., and Judge, G. (2019). Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series. Econometrics, 7.10.3390/econometrics7010010 ↗
36. A Multifault Diagnosis Method Based on Modified Sample Entropy for Lithium-Ion Battery Strings10.1016/j.jpowsour.2019.227275 ↗
37. Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring10.1016/j.ymssp.2006.02.009 ↗
38. Xu, Y., Chen, R., Li, Y., Zhang, P., Yang, J., Zhao, X., Liu, M., and Wu, D. (2019). Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model. Remote Sens., 11.10.3390/rs11232772 ↗
39. A Noise Suppression Method of Ground Penetrating Radar Based on EEMD and Permutation Entropy10.1109/LGRS.2019.2902123 ↗
40. Multi Feature Entropy Distance Approach with Vibration and Acoustic Emission Signals for Process Feature Recognition of Rolling Element Bearing Faults10.1177/1475921716687167 ↗
41. Incipient Fault Diagnosis of Rolling Bearings Based on Adaptive Variational Mode Decomposition and Teager Energy Operator10.1016/j.measurement.2019.106941 ↗
42. Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features
43. Improved Multi-Scale Entropy and It’s Application in Rolling Bearing Fault Feature Extraction10.1016/j.measurement.2019.107361 ↗
44. Yang, C., and Jia, M. (2020). Health Condition Identification for Rolling Bearing Based on Hierarchical Multiscale Symbolic Dynamic Entropy and Least Squares Support Tensor Machine–Based Binary Tree. Struct. Health Monit.10.1177/1475921720923973 ↗
45. Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis10.1109/ACCESS.2020.2992935 ↗
46. Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization10.1109/ACCESS.2020.2966582 ↗
47. Wang, H., and Du, W. (2020). Feature Extraction of Latent Fault Components of Rolling Bearing Based on Self-Learned Sparse Atomics and Frequency Band Entropy. J. Vib. Control.10.1177/1077546320925665 ↗
48. Wang, F., Zhang, Y., Zhang, B., and Su, W. (2011, January 27–28). Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend. Proceedings of the 2011 International Conference on Multimedia and Signal Processing, Toronto, ON, Canada.10.1109/CMSP.2011.93 ↗
49. Qin, X., Li, Q., Dong, X., and Lv, S. (2020, June 26). The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest. Available online: https://www.hindawi.com/journals/sv/2017/2623081/.10.1155/2017/2623081 ↗
50. Enhanced Frequency Band Entropy Method for Fault Feature Extraction of Rolling Element Bearings10.1109/TII.2019.2957936 ↗
51. Degradation Trend Estimation of Slewing Bearing Based on LSSVM Model10.1016/j.ymssp.2016.02.031 ↗
52. A Mathematical Theory of Communication10.1002/j.1538-7305.1948.tb01338.x ↗
53. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
54. Dispersion Entropy: A Measure for Time-Series Analysis10.1109/LSP.2016.2542881 ↗
55. Stochastic Entropy Production in the Quite Sun Magnetic Fields10.1093/mnrasl/sly211 ↗
56. Permutation Entropy: A Natural Complexity Measure for Time Series10.1103/PhysRevLett.88.174102 ↗
57. The Ordinal Kolmogorov-Sinai Entropy: A Generalized Approximation10.1016/j.cnsns.2016.11.001 ↗
58. A Spectral Entropy Method for Distinguishing Regular and Irregular Motion of Hamiltonian Systems10.1088/0305-4470/12/11/017 ↗
59. Quantification of EEG Irregularity by Use of the Entropy of the Power Spectrum10.1016/0013-4694(91)90138-T ↗
60. Entropy Analysis of the EEG Background Activity in Alzheimer\textquotesingles Disease Patients10.1088/0967-3334/27/3/003 ↗
61. Pan, Y.N., Chen, J., and Li, X.L. (2008). Spectral Entropy: A Complementary Index for Rolling Element Bearing Performance Degradation Assessment. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci.10.1243/09544062JMES1224 ↗
62. Harris, T., Rumbarger, J.H., and Butterfield, C.P. (2009). Wind Turbine Design Guideline DG03: Yaw and Pitch Rolling Bearing Life, Technical Report.10.2172/969722 ↗
63. Caesarendra, W., Pratama, M., Tjahjowidodo, T., Tieud, K., and Kosasih, B. (2018). Parsimonious Network Based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis. Appl. Sci., 8.10.3390/app8122656 ↗
64. Wang, S., Chen, J., Wang, H., and Zhang, D. (2019). Degradation Evaluation of Slewing Bearing Using HMM and Improved GRU. Measurement.10.1016/j.measurement.2019.06.038 ↗
65. Bao, W., Wang, H., Chen, J., Zhang, B., Ding, P., Wu, J., and He, P. (2019). Life Prediction of Slewing Bearing Based on Isometric Mapping and Fuzzy Support Vector Regression. Trans. Inst. Meas. Control.10.1177/0142331219862412 ↗
66. Liu, C., and Wang, F. (2017, January 10–12). A Review of Current Condition Monitoring and Fault Diagnosis Methods for Low-Speed and Heavy-Load Slewing Bearings. Proceedings of the 2017 9th International Conference on Modelling, Identification and Control, ICMIC, Kunming, China.10.1109/ICMIC.2017.8321619 ↗
67. Circular Domain Features Based Condition Monitoring for Low Speed Slewing Bearing10.1016/j.ymssp.2013.10.021 ↗
68. Rolling Element Bearing Diagnostics—A Tutorial10.1016/j.ymssp.2010.07.017 ↗