n-FFT压缩感知降维智能方法及其在齿轮系统故障特征提取与分类中应用研究

n-FFT COMPRESSED SENSING ALGORITHM OF SMART DIMENSIONALITY REDUCTION METHOD AND ITS APPLICATION IN FEATURE EXTRACTION AND CLASSIFICATION OF GEAR SYSTEM

机械传动被广泛应用在车辆、机床、工程机械等工程多个领域,因此研究机械传动的可靠性、损伤机理和故障诊断与信号处理方法具有重要意义和价值。传统的动态信号处理如FFT算法,即快速傅里叶变换法,因其运算速度快,谱分析特征稳定,所以得到了广泛的工程应用。但是工程实际中信号往往是非平稳的时变的非线性,非常复杂,再加之内外界噪声的干扰,分析处理较为困难,从而导致以FFT分析为主导的谱分析方法受到了很大限制。利用FFT运算速度快,特征稳定的优点,提出了对二次FFT,三次FFT算法,n次FFT算法(2n小于信号样本点数)进行探讨和研究的思想,从理论上进行探讨、并通过实验进行了验证,并将其应用于齿轮运行状态的故障特征监测和分类研究之中。分析研究表明n-FFT算法运行速度快,特征积聚度高,有利于进行齿轮运行状态的分类和故障监测与诊断。

Mechanical transmission is widely used in vehicles,machine tools,construction machinery and other engineering fields,the study of mechanical transmission reliability,damage mechanism,fault diagnosis and signal processing method has great significance and value.The traditional signal processing algorithms,such as FFT algorithm,the fast Fourier transform algorithm,since its fast speed,stable spectrum analysis,has been widely used in engineering. But the signals of engineering practice are very complex,nonlinear,and non-stationary,Within the external noise interference coupled,makes the analysis and processing more difficult,which lead to the spectral analysis based on FFT method has been very limited. For the advantages of computation speed,features and stability of FFT algorithm,the secondary FFT,third FFT algorithm,n-FFT algorithm( 2 npoints less than the signal sample points) were discussed in theory and research,the theory was explored and verified by experiment signals. It was applied in the classification study of gear signal. Analysis shows that n-FFT algorithm is fast,with high feature accumulation,is beneficial to the classify and fault monitoring and diagnosis of gear operating state.