摘要
针对微弱故障信号故障特征难以提取的问题,提出一种基于改进希尔伯特-黄变换的故障特征提取方法。该方法首先采用平均总体经验模态分解将故障信号分解成一系列固有模态函数,再选取对故障特征敏感的固有模态函数进行希尔伯特谱和边际谱分析,从中提取故障特征。仿真和实际试验证明:希尔伯特谱和边际谱能够清晰呈现故障信号时域和频域内的细微特性,为微弱故障信号的特征提取提供了一种切实可行的方法。
For solving the difficulty in extinction of weak fault signal, a method based on improved Hilbert- Huang transform is proposed. The weak fault signal is decomposed by ensemble empirical mode decomposition, and the intrinsic mode functions are obtained, then the sensitive intrinsic mode functions are selected by the sensitivity evaluation method. Finally, the Hilbert spectrum and marginal spectrum of the signals are obtained by the sensitive intrinsic mode functions, and the characteristics of the weak fault signal are detected. The sim- ulation and actual experiment results show that the Hilbert spectrum and marginal spectrum can display the subtle features corresponding to time and frequency of weak fault signals, and offered a practical method for its feature extraction.
出处
《东北电力大学学报》
2016年第5期52-56,共5页
Journal of Northeast Electric Power University
关键词
希尔伯特-黄变换
平均总体经验模态分解
微弱信号
特征提取
Hilbert-Huang transform
Ensemble empirical mode decomposition
Weak fault signal
Feature ex-traction
