基于RSGWPT-LCD的轴承信号故障特征提取及模式识别

为了有效提取滚动轴承振动信号的故障特征和提高分类识别精度,提出了一种基于冗余二代小波包变换-局部特征尺度分解(redundant second generation wavelet packet transform-local characteristic scale decomposition,简称RSGWPT-LCD)和极限学习机(extreme learning machine,简称ELM)相结合的故障特征提取和分类识别方法。首先,利用希尔伯特变换对原始振动信号进行处理,得到包络信号;其次,基于双层筛选机制,结合冗余二代小波包变换(redundant second generation wavelet packet transform,简称RSGWPT)和局部特征尺度分解(local characteristic-scale decomposition,简称LCD)方法对包络信号进行分解,筛选出包含主要信息的内禀尺度分量(intrinsic scale components,简称ISCs);然后,对提取的各ISCs分量构建初始特征矩阵并进行奇异值分解(singular value decomposition,简称SVD),将得到的奇异值作为表征各损伤信号的特征向量;最后,以提取的特征向量为输入样本,建立ELM模式分类器对滚动轴承损伤信号进行识别。信号仿真和实测数据表明,该方法可有效提取振动信号故障特征,提高分类识别精度,实现滚动轴承故障诊断。

Feature Extraction and Pattern Recognition of Fault Signals Generated from Rolling Element Bearings Based on RSGWPT-LCD and ELM

In order to effectively extract the fault features of vibration signal generated from the rolling element bearing and improve the accuracy of classification, a method of feature extraction and pattern recognition is proposed based on redundant second generation wavelet packet transform-local characteristic scale decomposition (RSGWPT-LCD) and extreme learning machine (ELM). The original vibration signal is first processed by Hilbert transform to obtain envelope signal. The RSGWPT with first-stage screening processes based on the energy ratio is taken as the pre-filter process unit to reduce random noises in the envelope signal, decomposes the signal into a series of narrow frequency bands and enhances the weak fault characteristic components in the different narrow frequency bands. Then, the selected feature packets are decomposed by LCD, and the second-stage screening processes are proposed to eliminate the pseudo components of intrinsic scale components (ISCs). Applying the spectrum analysis on those desired ISCs generated by the proposed method, the fault characteristics are easily extracted. Finally, singular value decomposition (SVD) is used to decompose the matrix which consists of desired ISCs to generate feature vectors. Feature vectors are input to ELM to specify the fault type. The proposed approach is evaluated by simulation and practical bearing vibration signals under different conditions. The experiment results show that the proposed approach is feasible and effective for the fault diagnosis of rolling element bearing.