一种自适应Chirplet分解方法及在故障诊断中应用

An adaptive Chirplet decomposition method and its application to the fault diagnosis

提出了一种自适应Chirplet信号分解过程中的参数优化估计方法,该方法首先利用最大投影分解原理结合分数阶傅立叶变换和拟牛顿方法进行参数估计,然后利用最大期望方法,进一步进行参数的优化。将提出的方法与传统的时频分析方法如短时傅立叶谱图,Wigner分布进行对比分析,仿真结果表明,提出的方法具有很高的参数估计精度、很高的时频图分辨率和抗噪能力。说明了本文提出的方法中引入EM算法的必要性。又将提出的方法应用到轴承的故障诊断中,实验结果表明,提出的自适应Chirplet分解方法是非常有效的。

A new approach of adaptive Chirplet parameter optimization is presented. In the proposed approach, via a combination of fractional Fourier transform and quasi-Newton method, the maximum projective decomposition is used to estimate the parameters. Then, the expectation maximization algorithm is used to refine the results. This method is compared with traditional time-frequency analysis methods, such as short-time Fourier spectrum and Wigner distribution, the simulation results show that this method can obtain more accurate estimation, finer time-frequency resolution and denoising capability, and the simulation also illustrate the necessity of EM. At last, the proposed method is applied to the fault diagnosis of bearing, the experiment results demonstrate that the proposed method is efficient.