基于多尺度线调频基稀疏信号分解的轴承故障诊断

Sparse Signal Decomposition Method Based on Multi-scale Chirplet and Its Application to Bearing Fault Diagnosis

在线调频小波路径追踪算法和稀疏信号分解的基础上,提出一种基于多尺度线调频基的稀疏信号分解方法,并将其应用于非平稳转速下的轴承故障诊断。基于多尺度线调频基的稀疏信号分解方法,根据信号的特点,自适应地选择多尺度的线调频基函数对信号进行投影分解。由于基函数库多尺度特性,使得该方法比以往采用单一尺度库函数的稀疏信号分解方法更适用于分解频率呈曲线变化的非平稳信号。在非恒定转速下,当轴承出现故障时,振动信号中与故障对应的特征频率将会随转速变化而波动,采用基于多尺度线调频基的稀疏信号分解方法能准确获得非平稳转速下轴承故障特征频率随时间的变化情况,进而对其状态和故障特征进行识别,仿真算例和应用实例说明了此方法的有效性。

Based on the chirplet path tracing algorithm and sparse signal decomposition, a new sparse signal decomposition method based on multi-scale chirplet is proposed and applied to the decomposition of bearing failure vibration signals under non-stationary speed. The proposed method projects the signals onto the multi-scale chirplet base functions, and chooses the base functions adaptively according to the signal characteristics. Because of the multi-scale features of the base functions, this method is superior to the old sparse signal decomposition method, which adopts a single scale, and is more applicable to the decomposition of non-stationary signals whose frequency has a curve change. When the bearing has a failure, the relevant characteristic frequency in the vibration signal will fluctuate with the change of speed. The proposed method is very suitable to obtain the bearing failure characteristic frequency which fluctuates with the time, so it can be used to identify the falures of bearings. Simulation and a practical application example proves the effectiveness of the method.