基于RQA和V-VPMCD的滚动轴承故障识别方法
Fault Recognition Method for Rolling Bearings Based on RQA and V-VPMCD

多变量预测模型模式识别(variable predictive model based class discriminate, 简称VPMCD)利用样本特征值内在的相关性来建立特征学习模型，但是当训练样本较少时会导致模型预测不准确，因此提出了基于递归定量分析（recurrence quantification analysis,简称RQA）和投票法多变量预测模型模式识别（voted variable predictive model based class discriminate,简称V-VPMCD）的故障识别方法。该方法利用了递归定量分析对非线性、非平稳信号分析的鲁棒性和样本质量不高时处理的优势，以VPMCD作为分类方法，并用投票法优化了VPMCD方法，提升了算法的稳定性和识别率。对滚动轴承不同程度、不同类型故障的模式识别实验表明，该优化算法具有较高的识别准确率和稳定性。
Variable predictive model based class discriminate (VPMCD) establishes the feature learning model by using the intrinsic correlation of the sample sets. The model can solve the problems with nonlinearity and high orders, but leads to inaccurate model prediction when the number of the samples is small. The thesis puts forward a fault recognition method based on the recurrence quantification analysis (RQA) and voted variable predictive model based class discriminate (V-VPMCD). This method applies strong robustness of RQA for the non-linear and non-stationary signals as well as the advantages for processing samples with inferior quality. Then, the prediction model based VPMCD is optimized by the method based on V-VPMCD. It has higher stability and recognition rate. The experiment-based fault recognition of different types and different degrees of rolling bearing demonstrates that the optimization algorithm has higher recognition rate and stability.