Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism

• Autorzy: Zhang Jiqiang , Kong Xiangwei , Cheng Liu , Qi Haochen , Yu Mingzhu

Identyfikatory

DOI

10.17531/ein.2023.1.16

• Języki publikacji: EN

Abstrakty

EN

Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.

Słowa kluczowe

EN

deep learning; continuous wavelet transform; improved channel attention mechanism; multi-conditions; convolutional neural network

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 1
• Strony: art. no. 16
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Zhang Jiqiang

• School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

• https://orcid.org/0000-0002-2856-0929

autor

Kong Xiangwei

• School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
• Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang 110819, China
• Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment, Northeastern University, Shenyang 110819, China

• https://orcid.org/0000-0001-7042-4368

autor

Cheng Liu

• School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

• https://orcid.org/0000-0002-7094-3835

autor

Qi Haochen

• School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

• https://orcid.org/0000-0002-9648-2141

autor

Yu Mingzhu

• Angang Steel Company Limited, Anshan 114021, China

• https://orcid.org/0000-0002-5549-9422

Bibliografia

• 1. Bai R, Xu Q, Meng Z et al. Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement 2021; 184, 109885, https://doi.org/10.1016/j.measurement.2021.109885.
• 2. Basaran M, Fidan M. Induction motor fault classification via entropy and column correlation features of 2D represented vibration data. Eksploatacja i Niezawodnosc –Maintenance and Reliability 2021; 23 (1): 132–142, http://dx.doi.org/10.17531/ein.2021.1.14.
• 3. Buzzoni M, Mucchi E, Dalpiaz G. A CWT-based methodology for piston slap experimental characterization. Mechanical Systems and Signal Processing 2017; 86 16-28, https://doi.org/10.1016/j.ymssp.2016.10.005.
• 4. Chen S, Yu J, Wang S. One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes. Journal of Process Control 2020; 87 54-67, https://doi.org/10.1016/j.jprocont.2020.01.004.
• 5. Dhiman H S, Deb D, Muyeen S M et al. Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines. IEEE Transactions on Energy Conversion 2021; 36 3462-3469, https://10.1109/TEC.2021.3075897.
• 6. Guo Y, Hu T, Zhou Y et al. Multi-channel data fusion and intelligent fault diagnosis based on deep learning. Measurement Science and Technology 2022; 34 015115, https:// 10.1088/1361-6501/ac8a64.
• 7. Hu A, Xiang L, Zhang Y. Experimental study on the intrawave frequency modulation characteristic of rotor rub and crack fault. Mechanical Systems and Signal Processing 2019; 118 209-225, https://doi.org/10.1016/j.ymssp.2018.08.051.
• 8. Hu Z X, Wang Y, Ge M F et al. Data-driven fault diagnosis method based on compressed sensing and improved multiscale network. IEEE Transactions on Industrial Electronics 2019;67 3216-3225, http://ieeexplore.ieee.org.doi: 10.1109/TIE.2019.2912763.
• 9. Huang W, Cheng J, Yang Y et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis. Neurocomputing 2019; 359 77-92, https://doi.org/10.1016/j.neucom.2019.05.052.
• 10. Jiang Y, Tang B, Qin Y et al. Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renewable energy 2011; 36 2146-2153, https://doi.org/10.1016/j.renene.2011.01.009.
• 11. Kong X, Li X, Zhou Q et al. Attention recurrent autoencoder hybrid model for early fault diagnosis of rotating machinery. IEEE Transactions on Instrumentation and Measurement 2021; 70 1-10, https://10.1109/TIM.2021.3051948.
• 12. Li J, Cao Y, Ying Y et al. A rolling element bearing fault diagnosis approach based on multifractal theory and gray relation theory. PLoS One 2016; 11 0167587, https://doi.org/10.1371/journal.pone.0167587.
• 13. Li J, Li X, He D et al. Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor. Journal of Intelligent Manufacturing 2020; 31 1899-1916, https://doi.org/10.1007/s10845-020-01543-8.
• 14. Li J, Li X, He D et al. Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor. Journal of Intelligent Manufacturing 2020; 31 1899-1916, https://doi.org/10.1007/s10845-020-01543-8.
• 15. Li X, Li J, Zhao C et al. Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection. Mechanical Systems and Signal Processing 2020; 142 106740, https://doi.org/10.1016/j.ymssp.2020.106740.
• 16. Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22 (1): 63–72, https:// 10.17531/ein.2020.1.8.
• 17. Liu H, Zhou J, Xu Y et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing 2018; 315 412-424, https://doi.org/10.1016/j.neucom.2018.07.034.
• 18. Liu R, Meng G, Yang B et al. Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine. IEEE Transactions on Industrial Informatics 2016; 13 1310-1320, https:// 10.1109/TII.2016.2645238.
• 19. Lu C, Wang Z Y, Qin W L et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing 2017; 130 377-388, https://doi.org/10.1016/j.sigpro.2016.07.028.
• 20. Nowakowski T, Komorski P. Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2022; 24 (1): 70–79, http://doi.org/10.17531/ein.2022.1.9.
• 21. Peng D, Wang H, Liu Z et al. Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition. IEEE Transactions on Industrial Informatics 2020; 16 4949-4960, https:// 10.1109/TII.2020.2967557.
• 22. Peng Z K, Peter W T, Chu F L. An improved Hilbert–Huang transform and its application in vibration signal analysis. Journal of sound and vibration 2005; 286 187-205, https://10.1016/j.jsv.2004.10.005.
• 23. Ravikumar K N, Madhusudana C K, Kumar et al. Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm. Engineering Science and Technology an International Journal 2022; 30 101048, https://doi.org/10.1016/j.jestch.2021.08.005.
• 24. Su W, Wang F, Zhu H et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement. Mechanical systems and signal processing 2010; 24 1458-1472, https://doi.org/10.1016/j.ymssp.2009.11.011.
• 25. Wang C, Gan M, Zhu C. A supervised sparsity-based wavelet feature for bearing fault diagnosis. Journal of Intelligent Manufacturing 2019; 30 229-239, https://doi.org/10.1007/s10845-016-1243-9.
• 26. Wang J, Li S, An Z et al. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing 2019; 329 53-65, https://doi.org/10.1016/j.neucom.2018.10.049.
• 27. Wang Q, Zhao B, Ma H et al. A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion. Journal of Mechanical Science and Technology 2019; 33 2561-2571, https:// 10.1007/s12206-019-0504-x.
• 28. Woo S, Park J, Lee J Y et al. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) 2018; 3-19, https://link.springer.com/conference/eccv. https://doi.org/10.1007/978-3-030-01234-2_1
• 29. Wu X, Zhang Y, Cheng C et al. A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery. Mechanical Systems and Signal Processing 2021; 149 107327, https://doi.org/10.1016/j.ymssp.2020.107327.
• 30. Xiong Q, Zhang W, Lu T et al. A fault diagnosis method for rolling bearings based on feature fusion of multifractal detrended fluctuation analysis and alpha stable distribution. Shock and Vibration 2016; https://doi.org/10.1155/2016/1232893.
• 31. Yang Y, Peng Z, Zhang W et al. Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances. Mechanical Systems and Signal Processing 2019; 119 182-221, https://doi.org/10.1016/j.ymssp.2018.07.039.
• 32. Ying Y, Li J, Chen Z et al.Study on rolling bearing online reliability analysis based on vibration information processing. Computers & Electrical Engineering 2018; 69 842-851, https://doi.org/10.1016/j.compeleceng.2017.11.029.
• 33. Yu J, Liu G. Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis. Knowledge-Based Systems 2020; 197 105883, https://doi.org/10.1016/j.knosys.2020.105883.
• 34. Zhang J, Kong X, Li X et al. Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout. Chinese Journal of Aeronautics 2022; 35(10):301-312, https://doi.org/10.1016/j.cja.2022.03.007.
• 35. Zhang J, Yi S, Liang G et al. A new bearing fault diagnosis method based on modified convolutional neural networks. Chinese Journal of Aeronautics 2020; 33 439-447, https://doi.org/10.1016/j.cja.2019.07.011.
• 36. Zhang K, Tang B, Qin Y et al. Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks. Mechanical Systems and Signal Processing 2019; 31 243-260, https://doi.org/10.1016/j.ymssp.2019.05.049.
• 37. Zhang R, Peng Z, Wu L et al. Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors 2017; 17 549, https://doi.org/10.3390/s17030549.
• 38. Zhu Z, Peng G, Chen Y et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing 2019; 323 62-75, https://doi.org/10.1016/j.neucom.2018.09.050.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).