Volume 49 Issue 4
Apr.  2023
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MA D,LIU Z H,GAO Q H,et al. Solenoid directional control valve fault pattern recognition based on multi-feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):913-921 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0367
Citation: MA D,LIU Z H,GAO Q H,et al. Solenoid directional control valve fault pattern recognition based on multi-feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):913-921 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0367

Solenoid directional control valve fault pattern recognition based on multi-feature fusion

doi: 10.13700/j.bh.1001-5965.2021.0367
Funds:  National Natural Science Foundation of China (51905541); Natural Science Basic Research Program of Shaanxi (2020JQ487); Young Talent Fund of University Association for Science and Technology in Shaanxi, China (20190412)
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  • Corresponding author: E-mail:liuzh_epgc@163.com
  • Received Date: 01 Jul 2021
  • Accepted Date: 01 Nov 2021
  • Available Online: 02 Jun 2023
  • Publish Date: 26 Nov 2021
  • In order to further improve the reliability and recognition accuracy of the solenoid valve fault diagnosis method based on current detection at the drive end, a research was conducted on the solenoid valve fault pattern recognition method. First, a method for extracting eigenvalues based on time-frequency analysis of current signals and time-domain parameters was proposed; then, through designing an acquisition experiment of the current signal at the solenoid valve drive end, the time domain signal of the solenoid valve drive end current and the multi-characteristic curve of the second-order rate of change were obtained. Meanwhile, the time-domain parameters and the frequency band energy corresponding to the second-order rate of change were extracted as the characteristic value, in order to construct the feature vector of multi-feature fusion. Finally, a multi-class support vector machine based on the radial basis kernel function was used to identify the electromagnetic directional valve pattern. The research results showed that compared with the support vector machine based on energy eigenvalues, the support vector machine based on multi-feature fusion can improve the recognition accuracy by 8.7% and the verification accuracy by 42.11%.

     

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  • [1]
    刘志浩, 高钦和, 牛海龙, 等. 基于驱动端电流检测的电磁阀故障诊断研究[J]. 兵工学报, 2014, 35(7): 1083-1090. doi: 10.3969/j.issn.1000-1093.2014.07.023

    LIU Z H, GAO Q H, NIU H L, et al. The fault diagnosis of electromagnetic valves based on driving current detection[J]. Acta Armamentarii, 2014, 35(7): 1083-1090(in Chinese). doi: 10.3969/j.issn.1000-1093.2014.07.023
    [2]
    蔡伟, 郑贤林, 张志利, 等. 液压电磁阀故障机理分析与瞬态特性仿真[J]. 仪器仪表学报, 2011, 32(12): 2726-2733. doi: 10.19650/j.cnki.cjsi.2011.12.014

    CAI W, ZHENG X L, ZHANG Z L, et al. Failure mechanism analysis and transient characteristic simulation of hydraulic electromagnetic valve[J]. Chinese Journal of Scientific Instrument, 2011, 32(12): 2726-2733(in Chinese). doi: 10.19650/j.cnki.cjsi.2011.12.014
    [3]
    孙昱, 何林. 基于电机电流信号的齿轮泵故障识别方法[J]. 机床与液压, 2021, 49(17): 191-195. doi: 10.3969/j.issn.1001-3881.2021.17.036

    SUN Y, HE L. Fault identification method of gear pump based on motor current signal[J]. Machine Tool & Hydraulics, 2021, 49(17): 191-195(in Chinese). doi: 10.3969/j.issn.1001-3881.2021.17.036
    [4]
    刘洋, 刘晓波, 梁珊. 基于傅里叶分解方法的航空发动机转子故障诊断[J]. 中国机械工程, 2019, 30(18): 2156-2163.

    LIU Y, LIU X B, LIANG S. Aeroengine rotor fault diagnosis based on Fourier decomposition method[J]. China Mechanical Engineering, 2019, 30(18): 2156-2163(in Chinese).
    [5]
    尹欣, 王雯. 基于小波包“能量-故障”的高温合金焊接缺陷特征值提取[J]. 煤矿机械, 2008, 29(11): 188-189. doi: 10.3969/j.issn.1003-0794.2008.11.084

    YIN X, WANG W. Defect features extraction of heat metal alloys based on “energy-fault” of wavelet packet[J]. Coal Mine Machinery, 2008, 29(11): 188-189(in Chinese). doi: 10.3969/j.issn.1003-0794.2008.11.084
    [6]
    张雪英, 任永梅, 贾海蓉. 用后验信噪比修正小波包自适应阈值的语音增强算法[J]. 中南大学学报(自然科学版), 2013, 44(11): 4566-4573.

    ZHANG X Y, REN Y M, JIA H R. Speech enhancement algorithm based on wavelet packet adaptive threshold revised by posteriori SNR[J]. Journal of Central South University (Science and Technology), 2013, 44(11): 4566-4573(in Chinese).
    [7]
    BRO R, SMILDE A K. Principal component analysis[J]. Analytical Methods, 2014, 6(9): 2812-2831. doi: 10.1039/C3AY41907J
    [8]
    SAINANI K L. Introduction to principal components analysis[J]. PM & R, 2014, 6(3): 275-278.
    [9]
    LI L M, ZHAO J, WANG C R, et al. Comprehensive evaluation of robotic global performance based on modified principal component analysis[J]. International Journal of Advanced Robotic Systems, 2020, 17(4): 172988141989688.
    [10]
    WANG L H, WU X Q, ZHANG C Y, et al. Hydraulic system fault diagnosis method based on a multi-feature fusion support vector machine[J]. The Journal of Engineering, 2019, 2019(13): 215-218. doi: 10.1049/joe.2018.9028
    [11]
    徐玉秀, 杨文平, 吕轩, 等. 基于支持向量机的汽车发动机故障诊断研究[J]. 振动与冲击, 2013, 32(8): 143-146. doi: 10.3969/j.issn.1000-3835.2013.08.024

    XU Y X, YANG W P, LYU X, et al. Fault diagnosis for a car engine based on support vector machine[J]. Journal of Vibration and Shock, 2013, 32(8): 143-146(in Chinese). doi: 10.3969/j.issn.1000-3835.2013.08.024
    [12]
    张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-42. doi: 10.16383/j.aas.2000.01.005

    ZHANG X G. Introduction to statistical learning theory and support vector machines[J]. Acta Automatica Sinica, 2000, 26(1): 32-42(in Chinese). doi: 10.16383/j.aas.2000.01.005
    [13]
    VAPNIK V N. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999. doi: 10.1109/72.788640
    [14]
    WESTON J, WATKINS C. Support vector machines for multi-class pattern recognition[C]// Proceedings of 7th European Symposium on Artificial Neural Networks, 1999: 219-224.
    [15]
    奉国和. SVM分类核函数及参数选择比较[J]. 计算机工程与应用, 2011, 47(3): 123-124. doi: 10.3778/j.issn.1002-8331.2011.03.037

    FENG G H. Parameter optimizing for support vector machines classification[J]. Computer Engineering and Applications, 2011, 47(3): 123-124(in Chinese). doi: 10.3778/j.issn.1002-8331.2011.03.037
    [16]
    赵薇, 靳聪, 涂中文, 等. 基于多特征融合的SVM声学场景分类算法研究[J]. 北京理工大学学报, 2020, 40(1): 69-75. doi: 10.15918/j.tbit1001-0645.2018.171

    ZHAO W, JIN C, TU Z W, et al. Support vector machine for acoustic scene classification algorithm research based on multi-features fusion[J]. Transactions of Beijing Institute of Technology, 2020, 40(1): 69-75(in Chinese). doi: 10.15918/j.tbit1001-0645.2018.171
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