改进希尔伯特-黄变换的发动机轴瓦故障诊断

Bearing Fault Diagnosis Based on Improved Hilbert-Huang Transform

针对希尔伯特-黄变换中经验模态分解方法存在的端点效应和虚假分量问题,提出一种改进的希尔伯特-黄变换方法,并将该方法应用于发动机轴瓦故障诊断中。以发动机声音信号为研究对象,首先利用端点优化对称延拓和镜像延拓联合法抑制端点效应,同时采用相关性分析法去除虚假分量,再通过快速独立分量分析法去除噪声等因素对诊断精确性的影响。通过试验研究表明,该方法能有效诊断出轴瓦损伤信号的故障原因,为轴瓦的故障诊断提供了一种全新、有效的方法。

Empirical mode decomposition has boundary effect and illusive component problem which effect its application. In this paper, an improved Hilbert-Huang transform method is studied and this method is applied to the fault diagnosis of engine bearing. The engine sound signal is analyzed by this technique. A method to depress the boundary is developed bases on boundary optimizing symmetrical extension method and mirror extension method, the correlation analysis is used to decide which the illusive component is, the signals are pretreated by using the fast independent component analysis method to eliminate ambient noise and other factors, and then the Hilbert spectrum of the signals are obtained with the improved Hilbert-Huang transform method. The experiment shows that this method can find the cause of bearing damage effectively and accurately. Provide an effective and a new method for this problem