A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment

Shujie Yang, Peikun Yang, Hao Yu, Jing Bai, Wuwei Feng, Yuxiang Su and Yulin Si
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Shujie Yang: School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316022, China
Peikun Yang: School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316022, China
Hao Yu: Institute of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zhoushan 316021, China
Jing Bai: School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316022, China
Wuwei Feng: School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316022, China
Yuxiang Su: School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316022, China
Yulin Si: Institute of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zhoushan 316021, China

Energies, 2022, vol. 15, issue 9, 1-16

Abstract: The vibration signals for offshore wind-turbine high-speed bearings are often contaminated with noises due to complex environmental and structural loads, which increase the difficulty of fault detection and diagnosis. In view of this problem, we propose a fault-diagnosis strategy with good noise immunity in this paper by integrating the two-dimensional convolutional neural network (2DCNN) with random forest (RF), which is supposed to utilize both CNN’s automatic feature-extraction capability and the robust discrimination performance of RF classifiers. More specifically, the raw 1D time-domain bearing-vibration signals are transformed into 2D grayscale images at first, which are then fed to the 2DCNN-RF model for fault diagnosis. At the same time, three procedures, including exponential linear unit (ELU), batch normalization (BN), and dropout, are introduced in the model to improve feature-extraction performance and the noise immune capability. In addition, when the 2DCNN feature extractor is trained, the obtained feature vectors are passed to the RF classifier to improve the classification accuracy and generalization ability of the model. The experimental results show that the diagnostic accuracy of the 2DCNN-RF model could achieve 99.548% on the CWRU high-speed bearing dataset, which outperforms the standard CNN and other standard machine-learning and deep-learning algorithms. Furthermore, when the vibration signals are polluted with noises, the 2DCNN-RF model, without retraining the model or any denoising process, still achieves satisfying performance with higher accuracy than the other methods.

Keywords: offshore wind turbine; high-speed bearing; fault diagnosis; CNN; RF; noise immunity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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