【摘要】 固有时间尺度分解(intrinsic time-scale decomposition,简称ITD)方法是一种新的自适应时频分析方法,其与经验模态分解(empirical mode decomposition,简称EMD)方法一样也有端点问题,且其分解得到的分量波形失真比较严重。为此,对ITD方法进行了改进,改进的ITD方法克服了上述缺点,并通过仿真信号的分析结果验证了改进后的ITD方法的有效性。把改进后的ITD方法与能量矩相结合应用于齿轮的故障诊断,首先,用改进的ITD方法将齿轮振动信号分解为若干个内禀尺度分量(intrinsic scale component,简称ISC);其次,将ISC分量进行基于时间轴的积分,得到ISC能量矩特征向量;最后,把特征向量输入支持向量机,从而判断齿轮是否有故障。实验数据的分析结果表明,该方法能有效地应用于齿轮的故障诊断。更多还原

【Abstract】 Intrinsic time-scale decomposition(ITD)is a new adaptive time-frequency analysis method. Both the ITD method and empirical mode decomposition(EMD)method have the end effect problems,and waveform distortion of the component obtained from ITD is more serious.An improved ITD method to overcome the above drawbacks is proposed in this paper.The improved method is used to analyze the simulation of signal and the results effectively prove this method.Moreover,the improved ITD method and energy moment are combined and applied to gear fault diagnosis.First of all,the improved method is used to decompose the gear vibration signal into a number of intrinsic scale components(ISC).Secondly,the ISC components are integrated based on time axis,and the energy moment can be gotten.Finally,the energy moment is put into support vector machine and the gear work state can be identified.Experiment results show that this method can be applied to the gear fault diagnosis effectively.更多还原