摘要
针对风电机组运行环境极端恶劣和运行工况复杂多变造成的故障特征无法准确、及时捕捉的特点,提出基于IMF(Intrinsic Mode Function)分量优化选取和Hilbert谱分析的风电轴承早期故障诊断方法。首先利用阶比重采样技术将时域非平稳信号转换为角域平稳或准平稳信号;然后对角域信号进行EMD(Empirical Mode Decomposition)分解,利用互相关准则和峭度准则选取IMF分量,重构角域信号;最后,采用希尔伯特变换对重构信号进行处理,得到边际谱。通过试验仿真验证了该方法的有效性。
For the extreme operating environment and variable working conditions of wind turbine and difficulty in finding fault feature accurately and promptly,a new incipient bearing fault method based on selecting optimal IMF （Intrinsic Mode Function）and Hilbert spectrum was proposed. Firstly,non-stationary time-domain signals were converted to stationary or quasi-stationary angle-domain signals;Secondly,the EMD （Empirical Mode Decom-position）method was used to decompose modal for angular waveform signal and obtain the IMF,and optimal IMF components were selected by cross-correlation criteria and kurtosis criteria to reconstruct signal. Finally,the re-construction signal was processed by using Hilbert transformation to obtain the marginal spectrum. The paper finally verified the effectiveness of the proposed method through experiment.
出处
《电力科学与工程》
2014年第3期39-44,共6页
Electric Power Science and Engineering
基金
国家自然科学基金资助项目(51075145)
华能科学技术项目(HNKJ-H27)
神华集团科技创新项目(GTKJ-12-02)
关键词
变工况特性
阶比重采样
IMF优化选取
边际谱
variational condition characteristic
order resampling
IMF optimal selection
marginal spectrum
