快速迭代滤波分解方法在轴承故障诊断中的应用

Application of a fast iterative filtering decomposition in bearing fault diagnosis

轴承故障信号通常是非线性和非平稳的。此外,这种信号非常微弱,容易被不可避免的背景噪声和振动干扰所掩盖。针对该种信号,模态分解方法已经被证实是一种可靠的处理方法。因此,将一种快速迭代滤波分解方法应用到轴承故障检测当中。快速迭代滤波分解方法在抑制模态混合和抗噪方面表现出色。与其他模态分解技术不同,快速迭代滤波分解方法具有超高的计算效率,因此可以明显提高计算速度。通过仿真信号和实际信号验证了该方法的有效性和优越性。

Bearing fault signals are usually non-linear and non-stationary. Also, this kind of signal is very weak and is easily concealed by unavoidable background noise and vibration interferences. The mode decomposition method has been proved to be a reliable signal processing method for this kind of signal. Therefore, a fast iterative filtering decomposition method is applied to the bearing fault detection in this paper. The fast iterative filtering decomposition method performs excellently in suppressing mode mixing and noise. More importantly, unlike other mode decomposition techniques, the fast iterative filtering method provides significant computational efficiency, so it can highly improve the computational speed. Both effectiveness and superiority of the proposed method are verified by simulation signals and real-world signals.