鉴于Hilbert-Huang变换(Hilbert-Huang Transformation,HHT)能根据信号的局部时变特征进行完全自适应分解,可以真实地表征非平稳、非线性信号,同时具有较高的时频分辨率和良好的时频聚集性,将HHT方法应用于煤矿通风机振动信号的分解,通过对边际谱、IMFs频谱和Hilbert谱的分析,以实现通风机的有效故障诊断。研究结果表明,该方法对通风机的振动故障诊断十分有效,对通风机运行状态监测具有一定的指导意义。 Since the Hilbert-Huang transform (HHT) can completely decompose according to the local time-varying features of the signal, it can truly characterize the non-stationary and nonlinear signals with high time-frequency resolution and good The HHT method is applied to the decomposition of coal mine ventilator vibration signals. Through the analysis of marginal spectrum, IMFs spectrum and Hilbert spectrum, the effective fault diagnosis of ventilator can be realized. The results show that this method is very effective in diagnosing the vibration of ventilator and is of guiding significance to the monitoring of ventilator operation status.