中国电力 ›› 2017, Vol. 50 ›› Issue (12): 153-158.DOI: 10.11930/j.issn.1004-9649.201702009

• 新能源 • 上一篇    下一篇

风电机组齿轮箱非平稳振动信号谱分析方法

丁显, 柳亦兵, 滕伟   

  1. 华北电力大学 电站设备状态检测与控制教育部重点实验室,北京 102206
  • 收稿日期:2017-02-03 出版日期:2017-12-20 发布日期:2018-01-30
  • 作者简介:丁显(1983—),男,河北石家庄人,博士研究生,从事新能源发电状态监测与数据分析研究。E-mail:fd_dingxian@163.com
  • 基金资助:
    国家自然科学基金资助项目(51775186,51305135)

Spectrum Analysis of Nonstationary Vibration Signal for Wind Turbine Gear Box

DING Xian, LIU Yibing, TENG Wei   

  1. Key Laboratory of Condition Monitoring and Control of Power Plant Equipment of Ministry of Education, North China Electric Power University, Beijing 102206, China
  • Received:2017-02-03 Online:2017-12-20 Published:2018-01-30
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No. 51775186, 51305135).

摘要: 齿轮箱是风电机组的重要部件,其运行状态直接决定了风电场的收益,通过研究齿轮箱的振动信号来评判齿轮箱的运行状态具有重要意义。论述了风电齿轮箱的结构形式和故障特征,依据某类型风电机组齿轮箱特点,制定振动数据采集方案,采集2台风电机组齿轮箱高速轴振动信号;应用Hilbert能量谱和短时傅里叶重排谱对比分析,分别提取2台机组齿轮箱高速轴测点振动信号中蕴含的故障特征频率。分析得到一台机组齿轮箱高速轴损伤,实际验证了该齿轮箱高速轴故障为齿面点蚀。证实了Hilbert能量谱和短时傅里叶重排谱相结合的分析方法在提取风电齿轮箱故障特征频率的有效性和实用性。

关键词: 风电机组, 齿轮箱, 非平稳信号, Hilbert能量谱, 短时傅里叶重排谱

Abstract: Gearbox is an important component of wind turbine, which directly determines wind farm revenue. It is of great significance to gearbox operational status evaluation by analyzing vibration signals. The structure and failure characteristics of wind turbine gearbox are discussed first. According to characteristics of one type of wind turbine gearbox, schemes for vibration data acquisition are established and high-speed shaft vibration signals from two wind turbine gearboxes are collected. Comparative analysis is performed by using Hilbert energy spectrum and short-time Fourier spectrum rearrangement methods to extract fault characteristic frequency from high-speed shaft measuring point vibration signal on two wind turbine gearboxes. Analysis results indicate that one gearbox has high-speed shaft damage which is verified by field test. The result confirms validity and practicability of combination of Hilbert energy spectrum and short-time Fourier spectrum rearrangement methods in extraction of wind turbine gearbox fault characteristic frequency.

Key words: wind turbine, gearbox, nonstationary signal, Hilbert energy spectrum, short-time Fourier spectrum rearrangement

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