摘要
针对发动机敲缸故障信号非平稳性并伴随强烈环境噪声的特点,提出基于改进希尔伯特-黄变换的故障诊断方法。该方法以发动机敲缸声音故障信号为研究对象,首先采用快速独立分量分析法将环境噪声等影响诊断准确性的因素从所采信号中分离,再利用总体平均经验模态分解和希尔伯特变换求出信号希尔伯特谱和边际谱,结合时域和频域特征进行故障诊断。通过仿真实验验证了所提方法的有效性,同时,实际试验证明:通过改进希尔伯特-黄变换方法所获得的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,为该类故障的诊断提供了一种切实可行的方法。
The engine abnormal sound signal is proved to be non-stationary and carries intense ambient noise. In view of these characteristics, a fault diagnosis method based on improved I-Iilbert-Huang Transform is proposed. The engine cylinder knocking sound signals as the research object, these signals are pretreated by using fast independent component analysis method to eliminate ambient noise and other factors, and then the Hilbert spectrum and marginal spectrum of the signals are obtained with ensemble empirical mode decomposition and Hilbert transform By combining the features of time domain and frequency domain, the faults can be diagnosed~ The simulation experiment shows the effectiveness of the proposed method. The actual test shows that the Hilbert spectrum and marginal spectrum can display the subtle features corresponding to time and frequency of fault signals, and offer a practical method for diagnosis.
出处
《机械设计与制造》
北大核心
2017年第12期72-76,共5页
Machinery Design & Manufacture
基金
吉林省教育厅科技发展项目(2014124)
关键词
希尔伯特-黄变换
总体平均经验模态分解
快速独立分量分析
敲缸
故障诊断
Hilbert-Huang Transform
Ensemble Empirical Mode Decomposition
Fast Independent Component An-alysis
Cylinder Knocking
Fault Diagnosis
