WIND TURBINE FAULT DIAGNOSIS METHOD BASED ON PARALLEL CONVOLUTIONAL NEURAL NETWORK

Meng Liang; Su Yuanhao; Xu Tongle; Kong Xiaojia; Lan Xiaosheng; Li Yunfeng

doi:10.19912/j.0254-0096.tynxb.2022-0052

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (5) : 449-456. DOI: 10.19912/j.0254-0096.tynxb.2022-0052

WIND TURBINE FAULT DIAGNOSIS METHOD BASED ON PARALLEL CONVOLUTIONAL NEURAL NETWORK

Meng Liang, Su Yuanhao, Xu Tongle, Kong Xiaojia, Lan Xiaosheng, Li Yunfeng

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Abstract

In view of the problems of difficulty in bearing weak fault signal feature extraction and poor performance of fault diagnosis model for wind turbine, a fault diagnosis method based on parallel convolutional neural network is proposed. Firstly, 1-Dimensional signals are converted into 2-dimensional time-frequency feature maps using continuous wavelet transform. Secondly, a parallel convolutional neural network structure is constructed, which consists of large convolutional layer and parallel convolutional layer. The large convolutional layer can quickly extract all the features of the input layer. The parallel convolutional layer is a two-layer small convolution parallel structure, which can effectively identify fault information. Then, the feature fusion layer is adopted to achieve feature enhancement inside the diagnosis model and reduce the complexity of the model, which combines the fault features extracted by two parallel convolutional layers. Finally, experimental verification showes that the fault diagnosis accuracy of the proposed model for bearing is 98.25%.

Key words

wind turbines / bearing / fault diagnosis / convolutional neural network / feature enhancement / feature visualzation

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Meng Liang, Su Yuanhao, Xu Tongle, Kong Xiaojia, Lan Xiaosheng, Li Yunfeng. WIND TURBINE FAULT DIAGNOSIS METHOD BASED ON PARALLEL CONVOLUTIONAL NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(5): 449-456 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0052

References

[1] LIU R N, YANG B Y, ZIO E, et al.Artificial intelligence for fault diagnosis of rotating machinery: a review[J]. Mechanical systems and signal processing, 2018, 108: 33-47.
[2] 胡超, 沈宝国, 谢中敏. 基于EMD-FastICA与DGA-ELM网络的轴承故障诊断方法[J]. 太阳能学报, 2021, 42(10): 208-219.
HU C, SHEN B G, XIE Z M.Fault diagnosis method of bearing based on EMD-FastICA and DGA-ELM network[J]. Acta energiae solaris sinica, 2021, 42(10): 208-219.
[3] HE Z Y, SHAO H D, WANG P, et al.Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples[J]. Knowledge-based systems, 2020, 191: 105313.
[4] MAO W T, DING L, TIAN S Y, et al.Online detection for bearing incipient fault based on deep transfer learning[J]. Measurement, 2020, 152: 107278.
[5] LI J M, YAO X F, WANG X D, et al.Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis[J]. Measurement, 2020, 153: 107419.
[6] XU T L, JI J Q, KONG X J, et al.Bearing fault diagnosis in the mixed domain based on crossover-mutation chaotic particle swarm[J]. Complexity, 2021, 2021(12):6632187.
[7] HE C, GE D C, YANG M H, et al.A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN[J]. Annals of nuclear energy, 2021, 159: 108326.
[8] GAO D W, ZHU Y S, REN Z J, et al.A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity[J]. Knowledge-based systems, 2021, 231: 107413.
[9] YE Z, YU J B.AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis[J]. Journal of manufacturing systems, 2021, 59: 467-480.
[10] ZHU Y, LI G P, WANG R, et al.Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization[J]. Applied acoustics, 2021, 183: 108336.
[11] LIANG P F, DENG C, WU J, et al.Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform[J]. Computers in industry, 2019, 113: 103132.
[12] XU Z F, LI C, YANG Y.Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks[J]. Applied soft computing, 2020, 95: 106515.
[13] CHENG Y Q, LIN M X, WU J, et al.Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network[J]. Knowledge-based systems, 2021, 216: 106796.
[14] 吴耀春, 赵荣珍, 靳伍银, 等. 利用DCNN融合多传感器特征的故障诊断方法[J]. 振动、测试与诊断, 2021, 41(2): 362-369.
WU Y C, ZHAO R Z, JIN W Y, et al.Mechanical fault diagnosis method based on multi-sensor signal feature fusion using deep convolutional neural network[J]. Journal of vibration, measurement & diagnosis, 2021, 41(2): 362-369.
[15] 张龙, 甄灿壮, 易剑昱, 等. 双通道特征融合CNN-GRU齿轮箱故障诊断[J]. 振动与冲击, 2021, 40(19): 239-245, 294.
ZHANG L, ZHEN C Z, YI J Y, et al.Dual-channel feature fusion CNN-GRU gearbox fault diagnosis[J]. Journal of vibration and shock, 2021, 40(19): 239-245, 294.
[16] LAURENS V D M, HINTON G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9: 2579-2605.