质量预测及故障诊断建模过程中非线性特征提取

Nonlinear Feature Extraction in Modeling of Quality Prediction and Fault Diagnosis

工业生产的质量预测及故障诊断建模过程中所涉及的特征数目大、复杂性高、非线性突出,造成了模型维数过高、时间复杂度高、计算精度下降.针对上述问题,提出了一种基于核主成分分析和粗糙集的特征提取方法,首先使用核主成分分析进行特征提取,再对提取出的特征用粗糙集进行约简,介绍了该方法的原理和具体实现步骤.并以某玻璃厂锡槽作业工艺为背景进行仿真实验,应用实际生产数据建立支持向量机的故障诊断模型,将应用核主成分分析、粗糙集及所提方法提取出的特征输入SVM诊断模型.对比三种方法的实验结果表明,基于核主成分分析和粗糙集的特征提取方法提取出的特征更优.

The features involved in modeling of quality prediction and fault diagnosis are in large number, complex and nonlinear, which makes the model compute in higher dimensions and time complexity, as well as a bad accuracy. To solve the above problems, a feature extraction method based on kernel principal component analysis （KPCA） and rough set （RS） was proposed. First, the features were extracted by the KPCA, and then reduced by the RS. Principle and specific implementation steps of the proposed method were introduced. In the end, simulations were carried out based on the operation process of tin bath in a glass manufactory, and SVM based model was established using the data captured in factual production as input. The features extracted from KPCA, RS and the proposed method were input into SVM models. The experimental results were compared with one another, which showed that the features extracted from integrated method of KPCA and RS were better than the others.