摘要
时变工况下行星齿轮箱的振动信号具有非平稳性和调频特点,常规频谱和解调分析方法难以识别故障特征频率。研究了基于自适应多尺度线性调频小波分解的行星齿轮箱故障诊断,利用自适应多尺度线性调频小波分解方法在分析时变调频信号方面的优势,以及基于线性调频小波的时频分布分辨率高、无交叉项干扰的优点,分析了行星齿轮箱时变工况下的故障实验信号,识别了时变的特征频率,诊断出了齿轮故障。
For planetary gearboxes under time-varying running conditions, their vibration signals have nonstationarity and frequency modulation feature. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. By exploiting the unique advantage of adaptive multi-scale chirplet decomposition in matching time varying frequency modulated signals, as well as the merits of time-frequency analysis derived from chirplet decomposition such as fine time-frequency resolution and free of cross-term interferences, the experimental signals of a wind turbine planetary gearbox under time-varying running condition were analyzed. In time-frequency domain, the time variant characteristic frequencies of gear fault were identified, and therefore the gear fault was diagnosed.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2013年第17期105-110,19,共6页
Proceedings of the CSEE
基金
国家自然科学基金项目(51075028
11272047)
北京市自然科学基金项目(3102022)
加拿大自然科学和工程研究理事会基金和加拿大安大略卓越中心基金项目~~
关键词
风力机
行星齿轮箱
故障诊断
非平稳
线性调频小波
时频分析
wind turbine
planetary gearbox
fault diagnosis
nonstationary
chirplet
time frequency analysis
