基于小波集成一维卷积神经网络的抗噪声聚变电源故障诊断方法研究

Noise-robust fusion power supply fault diagnosis based on wavelet integrated one-dimension convolutional neural network

数据驱动的电源故障诊断方法高度依赖于电源传感器的信号数据质量,托卡马克聚变装置中的电源系统往往在复杂电磁场耦合的环境下运行,导致其采集到的具有物理特征的信号常与大量无法解耦的噪声混合。为了抑制噪声对最终诊断结果的影响,提出了一种利用抗噪声小波增强一维卷积神经网络的多分支降噪网络(Hierarchy Branch Denoising Convolutional Neural Network,HBD-CNN),以完成噪声干扰下的电源系统故障诊断任务。具体而言,本研究将离散小波变换(Discrete Wavelet Transform,DWT)的信号分解功能植入CNN的网络层中,结合对噪声更加鲁棒的指数线性激活单元(Exponentially Linear Unit,ELU),对传统1D-CNN网络结构进行深度优化。此外,根据先验知识构建起的数据多层级结构,搭配网络中分层级的分类模块,提高了HBDCNN的泛化能力。最后,基于仿真电源数据集开展了对本模型架构的初步验证,当信噪比为10 dB时,对电源变换器的故障诊断准确率可达98.31%;当信噪比为2 dB时,准确率仍能保持92%以上。实验结果表明,HBDCNN在噪声工况下具有良好的故障诊断性能。

[Background]Data-driven methods for power fault diagnosis heavily rely on the signal data quality of power sensors.The power systems in Tokamak fusion devices often operate in environments with complex electromagnetic field coupling,leading to the mixing of physical characteristic signals with a significant amount of inseparable noise in the collected data.[Purpose]This study aims to mitigate the impact of noise on the final diagnostic results by proposing a multi-branch denoising network,termed Hierarchy Branch Denoising Convolutional Neural Network(HBD-CNN)that utilizes noise-resistant wavelet enhancement in conjunction with one-dimensional convolutional neural networks to accomplish power system fault diagnosis tasks under the influence of noise interference.[Methods]Firstly,the signal decomposition function of discrete wavelet transform(DWT)was incorporated into the network layer of the convolutional neural network(CNN),and the optimization of the traditional 1D-CNN network structure was deepened alongside the more robust exponentially linear unit(ELU)for noise.Then,a data multi-level structure was constructed based on prior knowledge to leverage and couple it with hierarchical classification modules within the network,hence the generalization capability of HBD-CNN was enhanced.Finally,preliminary validation of the architecture of this model was conducted based on the simulated power supply dataset.[Results]Validation results show that the fault diagnosis accuracy for the power converter reaches 98.31% when the signal-to-noise ratio(SNR)is 10 dB.Even at an SNR of 2 dB,the accuracy remains above 92%.[Conclusions]The results of this study indicate that HBD-CNN demonstrates excellent fault diagnosis performance and potential under noisy conditions.