基于 FastICA-BAS-MCKD 的滚动轴承复合故障特征提取方法

针对强背景噪声下轴承复合故障特征难以分离提取的问题,提出了一种基于快速独立成分分析-天牛须-最大相关峭度解卷积算法(FastICA-BAS-MCKD)的滚动轴承复合故障特征提取方法。 首先,引入 FastICA 对滚动轴承多通道故障信号进行盲源分离;其次,利用 BAS 算法同步优化 MCKD 算法的解卷积周期 T、滤波器长度 L 和移位数 M,构建基于 BAS-MCKD 的滚动轴承振动信号自适应分析方法;然后,应用 BAS-MCKD 方法处理分离后的信号,实现分离信号的降噪和特征增强;最后,应用希尔伯特解调方法对 MCKD 处理后的信号进行包络谱分析,实现滚动轴承不同类型故障的识别。 仿真和实测信号的分析结果表明,所提方法能清晰地从复合故障信号中提取出单一故障特征频率,为滚动轴承复合故障特征提取提供了一种有效的解决方案。

Compound fault feature extraction method of rolling bearingbased on FastICA-BAS-MCKD

Aiming at the problem that bearing composite fault features are difficult to be separated and extracted under strong backgroundnoise, a new compound faults diagnosis method is proposed in this paper based on fast independent component analysis ( FastICA),beetle antennae search algorithm ( BAS) and maximum correlated kurtosis deconvolution ( MCKD). Firstly, FastICA method isintroduced for blind separation of rolling bearing multi-channel fault signals. Secondly, the deconvolution period T, filter length L andshift number M of the deconvolution algorithm for MCKD are simultaneously optimized by using BAS. Then an adaptive analysis methodbased on BAS-MCKD for vibration signal of rolling bearing is constructed to achieve noise reduction and feature enhancement of separatedsignals. Finally, the Hilbert demodulation method is used to analyze the envelope spectrum of the signal processed by MCKD to realizethe identification of different types of rolling bearing faults. The analysis results of simulation and measured signals show that theproposed method can clearly extract the single fault characteristic frequency from the composite fault signal, which provides an effectivesolution for the complex fault characteristic extraction of rolling bearing.