参数化S谱幅值调制及轴承故障诊断应用

Parameterized S Spectral Amplitude Modulation and Its Applications to Bearing Fault Diagnosis

针对传统谱幅值调制方法易受噪声影响的问题,利用参数化S变换得到信号在时频域中的幅值,提出了一种参数化S谱幅值调制方法。该方法首先使用参数化S变换将信号转换到时频域并得到幅值和相位,然后将不同权重赋予时频域中的幅值以改变不同能量频率成分在信号中的占比,最后将调制后的幅值与原相位结合,使用参数化S逆变换重构一系列修正信号并计算其平方包络谱以提取故障特征。仿真和实验结果表明,该方法获得的幅值信息相比传统谱幅值调制方法更加准确和全面,对强噪声环境更具鲁棒性,能够有效实现滚动轴承的外圈、内圈和复合故障诊断。将所提方法与传统谱幅值调制方法和快速谱峭度方法进行对比,证明了参数化S谱幅值调制既能检测强噪声环境下的轴承故障信息,又能同时提取多种故障分量,在滚动轴承的故障特征提取中更具优越性。

To address the issue that traditional spectral amplitude modulation methods are susceptible to noise,this paper proposes a parameterized S spectral amplitude modulation method,where a parameterized S-transform is applied to obtain the amplitude of a signal in the time-frequency domain.First,the parameterized S-transform is used to convert the signal into the time-frequency domain and obtain its amplitude and phase information.Then,different weights are assigned to the amplitude in the time-frequency domain to alter the contribution of different energy frequency components in the signal.Lastly,the modulated amplitude is combined with the original phase,and the parameterized inverse S-transform is used to reconstruct a series of modified signals for computing squared envelope spectra and extracting fault characteristics.The simulation and experimental results indicate that the amplitude information obtained using the proposed method is more accurate and comprehensive compared to traditional spectral amplitude modulation methods.This method exhibits greater robustness in high-noise environments,and can effectively diagnose faults in the outer and inner rings of rolling bearings and compound faults.The proposed method is compared with traditional spectral amplitude modulation methods and fast Kurtogram.The results demonstrate that the parameterized S spectral amplitude modulation can not only detect bearing fault information in high-noise environments,but also extract multiple fault components simultaneously.Therefore,it exhibits obvious advantages in the diagnosis of rolling bearing faults.