基于信息融合的风电机组齿轮箱轴承故障诊断
作者:
中图分类号:

TM315

基金项目:

国家重点研发计划资助项目(2018YFB1501300);中央高校基本科研业务费(2019CDCGJX0017);重庆市科委资助项目(CSTC2019JSCX-MBDX0038)。


Fault diagnosis of wind turbines gearbox bearings based on information fusion
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [27]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    针对风电齿轮箱轴承故障问题,提出一种基于信息融合将BP神经网络与D-S证据理论相结合的风电轴承故障诊断方法。首先基于大数据,挖掘SCADA (supervisory control and data acquisition)系统中与风电齿轮箱轴承故障有关的振动、温度、电流、转矩和转速信号等故障特征;然后将各信号故障特征量作为神经网络输入,将神经网络的输出归一化作为证据理论基本概率分配值(BPA值),为解决各证据之间冲突问题,采用一种基于加权的方法来改进各条证据,以减小冲突;最后利用组合规则将各条改进的证据融合,得出最终诊断结果。研究基于某风场2 MW风电机组的实际运行数据,结果表明:随着融合信号维度的增加,最终诊断结果的准确率也逐步提高,融合多维信号的可靠性明显高于单一信号。

    Abstract:

    A fault diagnosis method for wind turbines gearbox bearings was proposed based on information fusion combining BP neural network and D-S evidence theory. Firstly, based on big data, the fault characteristics of vibration, temperature, current, torque and rotating speed signals related to the faults of wind turbines gearbox bearings in SCADA system were explored. Then, the fault feature quantity of each signal was used as the input of the neural network, and the outputs of the neural network were normalized as the Basic Probability Assignment (BPA value) of D-S evidence theory. In order to solve the conflict between evidences, a weighted-based improvement method was used to improve the evidence. Finally, the combination rules were used to fuse the improved evidences to obtain final diagnosis results. The study was based on actual operating data of a 2 MW wind turbine in a wind farm, and the results show that as the dimension of the fusion signal increases, the accuracy of the final diagnosis will gradually increase. The reliability of fusing multi-dimensional signals is significantly higher than that of single signals.

    参考文献
    [1] Chen J L, Pan J, Li Z P, et al. Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals[J]. Renewable Energy, 2016, 89:80-92.
    [2] Tian Z G, Jin T D, Wu B R, et al. Condition based maintenance optimization for wind power generation systems under continuous monitoring[J]. Renewable Energy, 2011, 36(5):1502-1509.
    [3] 雷亚国, 何正嘉, 林京, 等. 行星齿轮箱故障诊断技术的研究进展[J]. 机械工程学报,2011, 47(19):59-67.LEI Yaguo, HE Zhengjia, LIN Jing, et al. Research advances of fault diagnosis technique for planetary gearboxes[J]. Journal of Mechanical Engineering, 2011, 47(19):59-67. (in Chinese)
    [4] Bangalore P, Tjernberg L B. An artificial neural network approach for early fault detection of gearbox bearings[J]. IEEE Transactions on Smart Grid, 2015, 6(2):980-987.
    [5] Gelman L, Murray B, Patel T H, et al. Vibration diagnostics of rolling bearings by novel nonlinear non-stationary wavelet bicoherence technology[J]. Engineering Structures, 2014, 80:514-520.
    [6] Jena D P, Panigrahi S N. Automatic gear and bearing fault localization using vibration and acoustic signals[J]. Applied Acoustics, 2015, 98:20-33.
    [7] Janssens O, Schulz R, Slavkovikj V, et al. Thermal image based fault diagnosis for rotating machinery[J]. Infrared Physics & Technology, 2015, 73:78-87.
    [8] 潘泉, 王增福, 梁彦, 等. 信息融合理论的基本方法与进展(Ⅱ)[J]. 控制理论与应用, 2012, 29(10):1233-1244.PAN Quan, WANG Zengfu, LIANG Yan, et al. Basic methods and progress of information fusion (Ⅱ)[J]. Control Theory & Applications, 2012, 29(10):1233-1244. (in Chinese)
    [9] Safizadeh M S, Latifi S K. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell[J]. Information Fusion, 2014, 18:1-8.
    [10] Xu C M, Zhang H, Peng D G, et al. Study of fault diagnosis of integrate of D-S evidence theory based on neural network for turbine[J]. Energy Procedia, 2012, 16:2027-2032.
    [11] Li S B, Liu G K, Tang X H, et al. An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis[J]. Sensors, 2017, 17(8):1729.
    [12] 王致杰,徐余法,刘三明,等. 大型风力发电机组状态监测与智能故障诊断[M]. 上海:上海交通大学出版社, 2013.WANG Zhijie, XU Yufa, LIU Sanming, et al. State monitoring and intelligent fault diagnosis of large wind turbine[M]. Shanghai:Shanghai Jiao Tong University Press, 2013. (in Chinese)
    [13] Xiang Z, Zhang X N, Zhang W W, et al. Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO Spectrum and Stacking Auto-encoder[J]. Measurement, 2019, 138:162-174.
    [14] 冯新扬, 张巧荣, 李庆勇. 基于改进型深度网络数据融合的滚动轴承故障识别[J]. 重庆大学学报, 2019, 42(2):52-62.FENG Xinyang, ZHANG Qiaorong, LI Qingyong. Fault recognition of rolling bearing based on improved deep networks with data fusion in unbalanced data sets[J]. Journal of Chongqing University, 2019, 42(2):52-62. (in Chinese)
    [15] Eren L, Ince T, Kiranyaz S. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier[J]. Journal of Signal Processing Systems, 2019, 91(2):179-189.
    [16] Ren C, An N, Wang J Z, et al. Optimal parameters selection for BP neural network based on particle swarm optimization:a case study of wind speed forecasting[J]. Knowledge-Based Systems, 2014, 56:226-239.
    [17] 柯炎, 樊波, 谢一静, 等. 基于小波包分析和Elman神经网络的军用电源智能故障诊断[J]. 重庆大学学报, 2019, 42(9):67-73.KE Yan, FAN Bo, XIE Yijing, et al. Fault diagnosis of military power based on wavelet packet analysis and Elman neural network[J]. Journal of Chongqing University, 2019, 42(9):67-73. (in Chinese)
    [18] Quan H W, Li J H, Peng D L. Multisensor fault diagnosis based on data fusion using D-S theory[C/OL].Proceedings of the 33rd Chinese Control Conference. Piscataway, NJ:IEEE, 2014(2014-09-15)[2020-03-02]. https://doi.org/10.1109/ChiCC.2014.6896234.
    [19] Ye Q, Wu X P, Song Y X. Fault diagnosis method based on D-S theory of evidence and AHP[C/OL]. 2006 6th World Congress on Intelligent Control and Automation. Piscataway, NJ:IEEE, 2006(2006-10-23)[2020-04-25]. https://doi.org/10.1109/WCICA.2006.1714147.
    [20] Han X J, Zhang X L, Chen F, et al. Fault diagnosis method combining multi-relation indexes with D-S evidence theory[C/OL]. 2011 IEEE International Conference on Automation and Logistics (ICAL). Piscataway, NJ:IEEE, 2011(2011-09-23)[2020-03-02].https://doi.org/10.1109/ICAL.2011.6024690.
    [21] Jiang F, Li W, Wang Z Q, et al. Fault diagnosis of rotating machinery based on MFES and D-S evidence theory[C/OL]. 2012 24th Chinese Control and Decision Conference (CCDC). Piscataway, NJ:IEEE, 2012(2012-07-19)[2020-03-02].https://doi.org/10.1109/CCDC.2012.6243014.
    [22] 李松柏, 康子剑, 陶洁. 基于信息融合及堆栈降噪自编码的齿轮故障诊断[J]. 振动与冲击, 2019, 38(5):216-221.LI Songbai, KANG Zijian, TAO Jie. Gear fault diagnosis based on information fusion and stacked de-noising auto-encoder[J]. Journal of Vibration and Shock, 2019, 38(5):216-221. (in Chinese)
    [23] Huang J P, Liu W Y, Sun X M. A pavement crack detection method combining 2D with 3D information based on dempster-shafer theory[J]. Computer-Aided Civil and Infrastructure Engineering, 2014, 29(4):299-313.
    [24] Liu B. Three-dimensional aircraft recognition based on neural network and the D-S evidence theory[C/OL]. 2011 International Conference on Electrical and Control Engineering. Piscataway, NJ:IEEE, 2011(2011-10-24)[2020-03-02].https://doi.org/10.1109/ICECENG.2011.6058107.
    [25] 向阳辉, 张干清, 庞佑霞. 结合SVM和改进证据理论的多信息融合故障诊断[J]. 振动与冲击, 2015, 34(13):71-77.XIANG Yanghui, ZHANG Ganqing, PANG Youxia. Multi-information fusion fault diagnosis using SVM & improved evidence theory[J]. Journal of Vibration and Shock, 2015, 34(13):71-77. (in Chinese)
    [26] 杨风暴, 王肖霞. D-S证据理论的冲突证据合成方法[M]. 北京:国防工业出版社, 2010.YANG FengBao, WANG Xiaoxia. Combination method of conflictive evidences in D-S evidence theory[M]. Beijing:National Defense Industry Press, 2010. (in Chinese)
    [27] 李伟, 梁玉英, 朱赛. 基于神经网络和证据理论的信息融合在故障诊断中的应用[J]. 计算机测量与控制, 2012, 20(11):2888-2890, 2893.LI Wei, LIANG Yuying, ZHU Sai. Fault diagnosis based on neural network and evidence theory information fusion[J]. Computer Measurement & Control, 2012, 20(11):2888-2890, 2893. (in Chinese)
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

王屹立,朱才朝,鲁炯.基于信息融合的风电机组齿轮箱轴承故障诊断[J].重庆大学学报,2020,43(8):11-22.

复制
分享
文章指标
  • 点击次数:895
  • 下载次数: 1379
  • HTML阅读次数: 1098
  • 引用次数: 0
历史
  • 收稿日期:2020-03-02
  • 在线发布日期: 2020-08-25
文章二维码