改进多线性主成分分析网络及其在滚动轴承故障诊断中的应用

Fault Diagnosis Method of Rolling Bearings Based on Improved Multi-linear Principal Component Analysis Network

针对实测滚动轴承振动信号通常存在噪声干扰,具有非线性和非平稳特性,而多线性主成分分析网络(MPCAnet)在处理复杂非平稳数据时存在非线性拟合能力差、特征聚类性一般的问题,通过引入核变换,提出了一种改进的多线性主成分分析网络,增大了训练样本间的差异度,进一步提高了MPCAnet在处理非线性数据时的泛化能力和分类精度。通过不同滚动轴承故障诊断数据集对该方法进行验证,结果表明该方法具有较高的鲁棒性,能够准确识别滚动轴承的各类故障。

The measured rolling bearing vibration signals were usually interfered by noises and had nonlinear and non-stationary characteristics,while multi-linear principle component analysis network(MPCAnet)had poor nonlinear fitting ability and poor feature clustering ability when dealing with complex non-stationary data.An improved multi-linear principal component analysis network was proposed by introducing kernel transformation,which increased the degree of difference among the training samples,further enhanced the generalization ability and classification accuracy when dealing with non-linear data.It is proved that this method has high robustness in different fault diagnosis data sets of rolling bearings and may accurately identify various faults of rolling bearings.