nach oben

International Journal on Interactive Design and Manufacturing (IJIDeM)

Erschienen in:

08.12.2022 | Original Paper

Research on the effect of wind turbine bearing fault diagnosis method based on multi-feature calculation and Bayesian optimized machine learning method

verfasst von: Jiahui Jiang, Chaozheng Xu, Hexuan An

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 5/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Wind turbine bearings are one of the most important components of wind turbine generating equipment. Failure problems in wind turbine bearings can affect the operation of the entire plant. The data from sensors can be processed accurately and quickly through machine learning methods to diagnose the bearing failure. In this paper, six sets of experimental data are derived using a combination of feature extraction, principal component analysis, and Bayesian optimization of decision trees. Results are shown that the Bayesian optimized decision tree has higher diagnostic accuracy compared to the traditional decision tree. The principal component analysis method has some optimization effect on the original data, but the accuracy of the data after applying to feature extraction will be reduced. The Bayesian optimized decision tree based on feature extraction has the best results, with an accuracy of 99.8%. The findings of this paper have some reference value in the field of wind turbine bearing fault diagnosis in the future.

Vorheriger Artikel Optimum design of S-shaped diffuser by studying the effect of inlet velocity, turning angle and area ratio

Nächster Artikel Design and analysis of non-pneumatic tire with hybrid metamaterial spoke

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Rezamand, M., Kordestani, M., Carriveau, R., et al.: An integrated feature-based failure prognosis method for wind turbine bearings. IEEE/ASME Trans. Mechatron. 25(3), 1468–1478 (2020)CrossRef

Amirat, Y., Benbouzid, M.E.H., Al-Ahmar, E., et al.: A brief status on condition monitoring and fault diagnosis in wind energy conversion systems. Renew. Sustain. Energy Rev. 13(9), 2629–2636 (2009)CrossRef

Yang, D., Li, H., Hu, Y., et al.: Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion. Renew. Energy 92, 104–116 (2016)CrossRef

Li, Y., Liu, S., Shu, L.: Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data. Renew. Energy 134, 357–366 (2019)CrossRef

Kong, Z., Tang, B., Deng, L., et al.: Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units. Renew. Energy 146, 760–768 (2020)CrossRef

Chang, Y., Chen, J., Qu, C., et al.: Intelligent fault diagnosis of wind turbines via a deep learning network using parallel convolution layers with multi-scale kernels. Renew. Energy 153, 205–213 (2020)CrossRef

Chen, Z., Mauricio, A., Li, W., et al.: A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mech. Syst. Signal Process. 140, 106683 (2020)CrossRef

Zhi, L., Yuanliang, J., Zhongheng, X.: Anti-noise motor fault diagnosis method based on decision tree and the feature extraction methods in the time domain and frequency domain. In: 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, pp. 71–75 (2021)

Zhao, H., Zheng, J., Xu, J., et al.: Fault diagnosis method based on principal component analysis and broad learning system. IEEE Access 7, 99263–99272 (2019)CrossRef

10.

Lin J. Motor bearing fault diagnosis based on multi-domain feature extraction and improved optimized decision tree. ICCIA 218–225 (2020). https://doi.org/10.23977/ICCIA2020037

11.

Sun, W., Chen, J., Li, J.: Decision tree and PCA-based fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 21(3), 1300–1317 (2007)CrossRef

12.

Choi, S.W., Lee, I.B.: Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chem. Eng. Sci. 59(24), 5897–5908 (2004)CrossRef

13.

Nayana, B.R., Geethanjali, P.: Study of effective feature combination for fault diagnosis of bearings in motors. In: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). IEEE, pp. 1541–1546 (2020)

14.

Wang, X., Yan, X., He, Y.: Weak fault feature extraction and enhancement of wind turbine bearing based on OCYCBD and SVDD. Appl. Sci. 9(18), 3706 (2019)CrossRef

15.

Huang, Y., Zheng, W., Wang, X.D., Liang, Z., Gong, Z., Chen, H.: Analysis of power spot offer model based on martingale distance and density clustering. Power Syst. Autom. 45(13), 102–109 (2021). ((Problems in Engineering, 2021))

16.

Zhu, S., Xia, H., Annor-Nyarko, M., et al.: A robust strategy for sensor fault detection in nuclear power plants based on principal component analysis. Ann. Nucl. Energy 164, 108621 (2021)CrossRef

17.

Zhimei, Q., Yangxin, X., Yi, F., Hong, T., Xiaoyun, D., Zhu, F., Yanshan, H., Bin, W.: A decision tree based model for predicting the first inspection results of urban in-use vehicle environmental inspection. J. Environ. Sci. 41(04), 1574–1583 (2021). https://doi.org/10.13671/j.hjkxxb.2020.0444CrossRef

18.

Bull, A.D.: Convergence rates of efficient global optimization algorithms. J. Mach. Learn. Res. 12(10), 2879–2904 (2011)MathSciNetMATH

19.

Wu, J., Chen, X.-Y., et al.: Hyperparameter optimization for machine learning models based on bayesian optimization. J. Electron. Sci. Technol. 17, 26–40 (2019)

20.

Anowar, F., Sadaoui, S., Selim, B.: Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Comput. Sci. Rev. 40, 100378 (2021)MathSciNetCrossRefMATH

Titel: Research on the effect of wind turbine bearing fault diagnosis method based on multi-feature calculation and Bayesian optimized machine learning method
verfasst von: Jiahui Jiang
Chaozheng Xu
Hexuan An
Publikationsdatum: 08.12.2022
Verlag: Springer Paris
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 5/2023
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI: https://doi.org/10.1007/s12008-022-01085-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 5/2023

Effect of interlock angle and bottom die flange diameter on clinching joint load bearing capacity in cross-tensile loading

Effects of temperature, die angle and number of passes on the extrusion of 6063 aluminium alloy: experimental and numerical study

Friction stir spot welding process: an innovative approach for transforming from engineering design to production

Numerical simulation of sand–water slurry flow through pipe bend using CFD

Development of tool electrode by powder metallurgy technique and analysis of tool wear rate by design of experiment methodology

Effect of porosity inclusions on the natural frequencies of the FGM plates using dynamic stiffness method

Premium Partner