[1]张树忠.风机齿轮箱故障诊断系统的设计与实现[J].福建工程学院学报,2018,16(06):516-519.[doi:10.3969/j.issn.1672-4348.2018.06.002]
　ZHANG Shuzhong.Design and realization of fault diagnosis system for the wind turbine’s gearbox[J].Journal of FuJian University of Technology,2018,16(06):516-519.[doi:10.3969/j.issn.1672-4348.2018.06.002]
点击复制

风机齿轮箱故障诊断系统的设计与实现()
分享到：

《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第16卷
期数:
2018年06期
页码:
516-519
栏目:
出版日期:
2018-12-25

文章信息/Info

Title:
Design and realization of fault diagnosis system for the wind turbine’s gearbox
作者:
张树忠
福建工程学院机械与汽车工程学院
Author(s):
ZHANG Shuzhong
School of Mechanical and Automotive Engineering, Fujian University of Technology
关键词:
风力发电机齿轮箱; 故障诊断; 小波算法; BP神经网络
Keywords:
wind turbine gearbox;  fault diagnose;  wavelet algorithm;  BP neutral network
分类号:
TH16
DOI:
10.3969/j.issn.1672-4348.2018.06.002
文献标志码:
A
摘要:
针对风力发电机关键部件齿轮箱故障频率高、诊断难的问题，开发了包含传统时频域分析和现代分析模块的故障诊断系统。该系统对所采集的振动信号进行了小波去噪、小波包分解并重构后得到各频段能量占比的特征向量，将该特征向量输入到Back Propagation（BP）神经网络模型进行振动信号与正常或各故障状态之间映射，从而智能识别运行状态。应用Matlab和Labview开发系统，输入齿轮箱的4种典型运行状态进行验证，结果表明，所设计的系统可较好地对风机齿轮箱的故障进行诊断。
Abstract:
Aiming at the issues of high failure possibility and difficult diagnosis of wind turbine gearbox, a fault diagnosis system including traditional time-frequency domain analysis and modern analysis modules was developed. After collecting the vibration signals, the system performs wavelet denoising, wavelet packet decomposition and reconstruction to obtain the eigenvectors of the energy ratio of each frequency band. The eigenvectors are fed into the Back Propagation (BP） neural network model for mapping between the vibration signals and the normal and fault states, so as to automatically detect the running states. Matlab and Labview were used to develop the system. Four typical running states of the gearbox were used as input to verify the results. Results show that the designed system can better diagnose the fault of the wind turbine gearbox.

参考文献/References:

[1] 谢源, 焦斌. 风力发电机组状态监测系统与故障诊断方法研究现状[J]. 上海电机学院学报, 2010, 13(6): 328-333.
[2] MOHANTY A R. Fault detection in a multistage gearbox by demodulation of motor current waveform[J]. IEEE Trans Industrial Electronics, 2006, 53(4): 1285-1297.
[3] 曲弋. MW级风力发电机组关键部件振动分析与故障诊断方法研究[D]. 沈阳: 沈阳工业大学, 2012.
[4] 刘景浩. 齿轮传动故障诊断专家系统的研究与应用[D]. 重庆: 重庆大学, 2005.
[5] JOHNSON K, WINGERDEN J-W V, BALAS M J, et al. Special issue on “past, present and future modeling and control of wind turbines”[J]. Mechatronics, 2011, 21(4): 633.
[6] ZHONG X Y, ZENG L C, ZHAO C H, et al. Research of condition monitoring and fault diagnosis techniques for wind turbine gearbox[J]. Applied Mechanics and Materials, 2012, 197: 206-210.
[7] 张彦创. 风电机组状态监测与故障诊断系统的设计与实现[D]. 吉林: 吉林大学, 2013.
[8] 张新疆. 基于LabVIEW的风机齿轮箱离线故障诊断系统研究[D]. 成都: 电子科技大学, 2013.
[9] 易雄. 基于小波分析的机械故障特征提取与诊断技术研究[D]. 杭州: 浙江工业大学, 2009.

相似文献/References:

[1]董志文,苏晶晶.基于VMD-MTF-CNN的故障电弧检测方法[J].福建工程学院学报,2024,22(04):371.[doi:10.3969/j.issn.2097-3853.2024.04.010]
　DONG Zhiwen,SU Jingjing.Arc fault detection method based on VMD-MTF-CNN[J].Journal of FuJian University of Technology,2024,22(06):371.[doi:10.3969/j.issn.2097-3853.2024.04.010]
[2]傅锦涛,张弓,张树忠.基于迁移学习的磨倒机轴承故障诊断[J].福建工程学院学报,2024,22(04):393.[doi:10.3969/j.issn.2097-3853.2024.04.013]
　FU Jintao,ZHANG Gong,ZHANG Shuzhong.Bearing fault diagnosis of cross-working grinding mill based on transfer learning[J].Journal of FuJian University of Technology,2024,22(06):393.[doi:10.3969/j.issn.2097-3853.2024.04.013]

更新日期/Last Update: 2018-12-25