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NUCLEAR TECHNIQUES, Volume. 47, Issue 5, 050015(2024)

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

Qin HANG1, Lingpeng ZHONG1, Hua LI2, and Heng ZHANG1、*
Author Affiliations
  • 1College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • 2Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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    [1] Deng X, Wan C G, Jiang L et al. Open-switch fault diagnosis of three-phase PWM converter systems for magnet power supply on EAST[J]. IEEE Transactions on Power Electronics, 38, 1064-1078(2023).

    [2] Huang H H, Cheng W, Wang H X et al. Development of new fast control power supply for EAST[J]. IEEE Transactions on Applied Superconductivity, 23, 4201806(2013).

    [3] Pang Y H, Yang D S, Teng R et al. A deep learning based multiple signals fusion architecture for power system fault diagnosis[J]. Sustainable Energy, Grids and Networks, 30, 100660(2022).

    [4] Tian A S, Zhang Y, Ma C et al. Noise-robust machinery fault diagnosis based on self-attention mechanism in wavelet domain[J]. Measurement, 207, 112327(2023).

    [5] Zhang W, Li C H, Peng G L et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing, 100, 439-453(2018).

    [6] Chen X H, Zhang B K, Gao D. Bearing fault diagnosis base on multi-scale CNN and LSTM model[J]. Journal of Intelligent Manufacturing, 32, 971-987(2021).

    [7] Zhang W, Peng G L, Li C H et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 17, 425(2017).

    [8] Magar R, Ghule L, Li J H et al. FaultNet: a deep convolutional neural network for bearing fault classification[J]. IEEE Access, 9, 2-25199(2021).

    [9] Wu Y H, Sun X B, Zhang Y et al. A power transformer fault diagnosis method-based hybrid improved seagull optimization algorithm and support vector machine[J]. IEEE Access, 10, 1-17286(2022).

    [10] Guan M J, Liu C, Liu P L et al. Fault diagnosis scheme for open-circuit fault in N+2 converter of six-phase SRM using midpoint current spectrum analysis[J]. IEEE Access, 10, 105983-105992(2022).

    [11] Cherif B D E, Bendiabdellah A. Detection of two-level inverter open-circuit fault using a combined DWT-NN approach[J]. Journal of Control Science and Engineering, 2018, 1976836(2018).

    [12] Qiao M Y, Yan S H, Tang X X et al. Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads[J]. IEEE Access, 8, 6-66269(2020).

    [13] Peng B S, Xia H, Lv X Z et al. An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network[J]. Applied Intelligence, 52, 3051-3065(2022).

    [14] Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693(1989).

    [15] YANG Xiaoyan, HONG Xu, ZHOU Jianbin et al. Gaussian pulse shaping algorithm for dual exponential signals based on wavelet transform[J]. Nuclear Techniques, 46, 050403(2023).

    [16] SUN Kun, XIONG Chao, GE Liangquan et al. Line unit correction method of airborne gamma-ray spectrum anomaly information based on wavelet transform[J]. Nuclear Techniques, 41, 100501(2018).

    [17] SHI Rui, Xianguo TUO, ZHANG Jinzhao et al. Smoothing and evaluation of low level alpha spectra based on DWT method[J]. Nuclear Techniques, 36, 120201(2013).

    [18] Li Q F, Shen L L, Guo S et al. Wavelet integrated CNNs for noise-robust image classification[C], 7243-7252(2020).

    [19] Yuan H W, Fu P, Gao G et al. On the circulating current control of ITER poloidal field converter[J]. Journal of Fusion Energy, 33, 269-274(2014).

    [20] Su K G, Liu J H, Xiong H. Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions[J]. Knowledge-Based Systems, 230, 107386(2021).

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    Qin HANG, Lingpeng ZHONG, Hua LI, Heng ZHANG. Noise-robust fusion power supply fault diagnosis based on wavelet integrated one-dimension convolutional neural network[J]. NUCLEAR TECHNIQUES, 2024, 47(5): 050015

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    Paper Information

    Category: Research Articles

    Received: Apr. 8, 2024

    Accepted: --

    Published Online: Jul. 8, 2024

    The Author Email: ZHANG Heng (张恒)

    DOI:10.11889/j.0253-3219.2024.hjs.47.050015

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology