BEARING FAULT DIAGNOSIS METHOD FOR IMBALANCED DATA USING WLT-GAN

Jiao Huachao; Sun Wenlei; Wang Hongwei; Wan Xiaojing

doi:10.19912/j.0254-0096.tynxb.2024-2035

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 392-401. DOI: 10.19912/j.0254-0096.tynxb.2024-2035

BEARING FAULT DIAGNOSIS METHOD FOR IMBALANCED DATA USING WLT-GAN

Jiao Huachao, Sun Wenlei, Wang Hongwei, Wan Xiaojing

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Abstract

To address the degradation in fault diagnosis accuracy caused by the imbalance of bearing fault data, this paper proposes a fault diagnosis method based on a wavelet-like transform generative adversarial network （WLT-GAN）. In the proposed method, the wavelet-like transform neural network is embedded into the generator and combined with a dual-discriminator architecture, enabling the WLT-GAN to jointly learn time-domain and frequency-domain features from vibration signals and generate high-quality fault samples to effectively alleviate data imbalance. In addition, an ensemble learning strategy is employed to construct the fault diagnosis model, where a soft-voting mechanism integrates multi-source features to improve diagnostic accuracy. Experimental results demonstrate that the samples generated by WLT-GAN exhibit high similarity to real data in both time- and frequency-domain feature distributions. Leveraging the advantages of ensemble learning, the proposed method achieves high accuracy and robustness, providing an efficient and reliable solution for bearing fault diagnosis in wind turbine generators.

Key words

wind turbine bearings / imbalanced data / fault diagnosis / generative adversarial network / wavelet-like transform / ensemble learning

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Jiao Huachao, Sun Wenlei, Wang Hongwei, Wan Xiaojing. BEARING FAULT DIAGNOSIS METHOD FOR IMBALANCED DATA USING WLT-GAN[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 392-401 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2035

References

[1] 司伟伟, 岑健, 伍银波, 等. 小样本轴承故障诊断研究综述[J]. 计算机工程与应用, 2023, 59(6): 45-56.
SI W W, CEN J, WU Y B, et al.Review of research on bearing fault diagnosis with small samples[J]. Computer engineering and applications, 2023, 59(6): 45-56.
[2] CHEN X H, YANG R, XUE Y H, et al.Deep transfer learning for bearing fault diagnosis: a systematic review since 2016[J]. IEEE transactions on instrumentation and measurement, 2023, 72: 3508221.
[3] 和林芳, 王道涵, 田淼, 等. 基于IBCAN的风力发电机轴承故障诊断方法研究[J]. 太阳能学报, 2025, 46(1): 97-104.
HE L F, WANG D H, TIAN M, et al.Research on fault diagnosis method of wind turbine bearing based on IBCAN[J]. Acta energiae solaris sinica, 2025, 46(1): 97-104.
[4] 陈强, 钱洵. 基于谐波消除和谱峭度的风电机组主轴承故障诊断[J]. 太阳能学报, 2025, 46(1): 1-8.
CHEN Q, QIAN X.Fault diagnosis of wind turbine main bearings based on harmonic removal and spectral kurtosis[J]. Acta energiae solaris sinica, 2025, 46(1): 1-8.
[5] GAO S Z, SHI S, ZHANG Y M.Rolling bearing compound fault diagnosis based on parameter optimization MCKD and convolutional neural network[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 3508108.
[6] 刘俊孚, 岑健, 黄汉坤, 等. 零小样本旋转机械故障诊断综述[J]. 计算机工程与应用, 2024, 60(15): 42-54.
LIU J F, CEN J, HUANG H K, et al.Review on zero or few sample rotating machinery fault diagnosis[J]. Computer engineering and applications, 2024, 60(15): 42-54.
[7] 苏元浩, 孟良, 许同乐, 等. 不平衡数据集下优化WGAN的风电机组齿轮箱故障诊断方法[J]. 太阳能学报, 2022, 43(11): 148-155.
SU Y H, MENG L, XU T L, et al.Wind turbine gearbox fault diagnosis method for optimized WGAN with unbalanced data sets[J]. Acta energiae solaris sinica, 2022, 43(11): 148-155.
[8] 郭伟, 邢晓松. 基于改进卷积生成对抗网络的少样本轴承智能诊断方法[J]. 中国机械工程, 2022, 33(19): 2347-2355.
GUO W, XING X S.Intelligent fault diagnosis of bearings with few samples based on an improved convolutional generative adversarial network[J]. China mechanical engineering, 2022, 33(19): 2347-2355.
[9] 李俊卿, 胡晓东, 王罗, 等. 考虑数据不足和基于合作博弈模型融合的风电机组轴承故障诊断方法[J]. 太阳能学报, 2024, 45(1): 234-241.
LI J Q, HU X D, WANG L, et al.Bearing fault diagnosis method for wind turbine considering insufficient data and based on cooperative game model fusion[J]. Acta energiae solaris sinica, 2024, 45(1): 234-241.
[10] 张永宏, 张中洋, 赵晓平, 等. 基于VAE-GAN和FLCNN的不均衡样本轴承故障诊断方法[J]. 振动与冲击, 2022, 41(9): 199-209.
ZHANG Y H, ZHANG Z Y, ZHAO X P, et al.Bearing fault diagnosis method based on VAE-GAN and FLCNN unbalanced samples[J]. Journal of vibration and shock, 2022, 41(9): 199-209.
[11] 何强, 唐向红, 李传江, 等. 负载不平衡下小样本数据的轴承故障诊断[J]. 中国机械工程, 2021, 32(10): 1164-1171, 1180.
HE Q, TANG X H, LI C J, et al.Bearing fault diagnosis method based on small sample data under unbalanced loads[J]. China mechanical engineering, 2021, 32(10): 1164-1171, 1180.
[12] 郭俊锋, 王淼生, 孙磊, 等. 基于生成对抗网络的滚动轴承不平衡数据集故障诊断新方法[J]. 计算机集成制造系统, 2022, 28(9): 2825-2835.
GUO J F, WANG M S, SUN L, et al.New method of fault diagnosis for rolling bearing imbalance data set based on generative adversarial network[J]. Computer integrated manufacturing systems, 2022, 28(9): 2825-2835.
[13] LIAO W Q, YANG K, FU W L, et al.A review: the application of generative adversarial network for mechanical fault diagnosis[J]. Measurement science and technology, 2024, 35(6): 062002.
[14] RUAN D W, CHEN X R, GÜHMANN C, et al. Improvement of generative adversarial network and its application in bearing fault diagnosis: a review[J]. Lubricants, 2023, 11(2): 74.
[15] 陈鹏. 基于振动信号的滚动轴承故障诊断方法综述[J]. 轴承, 2022(6): 1-6.
CHEN P.Review on fault diagnosis methods for rolling bearings based on vibration signals[J]. Bearing, 2022(6): 1-6.
[16] CERRADA M, SÁNCHEZ R V, LI C, et al. A review on data-driven fault severity assessment in rolling bearings[J]. Mechanical systems and signal processing, 2018, 99: 169-196.
[17] WANG H Y, LI P, LANG X, et al.FTGAN: a novel GAN-based data augmentation method coupled time-frequency domain for imbalanced bearing fault diagnosis[J]. IEEE transactions on instrumentation and measurement, 2023, 72: 3502614.
[18] 李川, 伍依凡, 杨帅. 不平衡分布的数据驱动故障诊断的研究进展[J]. 仪器仪表学报, 2023, 44(8): 181-197.
LI C, WU Y F, YANG S.Research progress of fault diagnostics driven by imbalanced data distribution[J]. Chinese journal of scientific instrument, 2023, 44(8): 181-197.
[19] 钟宏宇. 面间小样本和轻量化的轴承智能故障诊断方法研究[D]. 武汉: 武汉科技大学, 2024.
ZHONG H Y.Bearing intelligent fault diagnosis method for small sample and lightweight[D]. Wuhan: Wuhan University of Science and Technology, 2024.
[20] 张志明. 游梁式抽油机电机滚动轴承故障诊断方法研究[D]. 大庆: 东北石油大学, 2024.
ZHANG Z M.Research on fault diagnosis method for rolling bearing of motor in beam pumping unit[D]. Daqing: Northeast Petroleum University, 2024.
[21] STENGER M, LEPPICH R, FOSTER I, et al.Evaluation is key: a survey on evaluation measures for synthetic time series[J]. Journal of big data, 2024, 11(1): 66.
[22] YIN W Z, XIA H, HUANG X Y, et al.A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples[J]. Annals of nuclear energy, 2024, 199: 110340.
[23] 闫向彤, 罗嘉伟, 曹现刚. WGAN-GP结合CBAM-VGG16轻量化网络滚动轴承故障诊断[J]. 噪声与振动控制, 2024, 44(5): 120-127.
YAN X T, LUO J W, CAO X G.Rolling bearing fault diagnosis with WGAN-GP and lightweight CBAM-VGG16 network[J]. Noise and vibration control, 2024, 44(5): 120-127.