基于局部平衡判别投影的旋转机械故障诊断方法

针对旋转机械故障数据的多类别、高维复杂特性导致的分类困难问题,提出一种基于局部平衡判别投影(LBDP)的故障数据集降维方法.从时域、频域和时频域多个角度提取转子振动信号的混合特征,构建原始高维故障特征集;通过LBDP选择出其中最能反映故障本质的敏感特征子集;将得到的低维特征子集输入到K近邻分类器(KNN)中进行故障模式...

A Fault Diagnosis Method of Rotating Machinery Based on LBDP

Aiming at the problems of classification difficulty caused by multi-class and high-dimensional complex characteristics of rotor fault data, a LBDP dimensionality reduction algorithm was proposed. First of all,the mixed features of the rotor vibration signals were extracted from multiple angles in time domain, frequency domain and time-frequency domain,and the high-dimensional feature sets were constructed. The original feature sets were fused by LBDP algorithm, and the low-dimensional sensitive feature subsets which might best reflect the intrinsic information of the faults were selected. Then the low-dimensional feature subsets were input into K-nearest neighbor(KNN) classifier for training and fault classification. The effectiveness of the proposed method was verified by the vibration signal sets of a double-span rotor systems, and it is proved that the method may extract the local discriminant information comprehensively and make the difference among fault categories clearer.