To address the issue of online monitoring and fault feature extraction in multi-modal operation process of condenser equipment,a fault feature extraction method integrating k-means clustering and re-constructed principal component analysis was proposed.First,k-means clustering was used to identify modes.The clustering results show significant differences in squared prediction error(SPE)statistics a-mong different modes,and it is necessary to establish monitoring models for sub-modes.In order to obtain fault features effectively and suppress residual pollution in fault separation process,fault feature vectors were obtained by reconstructing principal component analysis method,and the fault feature extraction for multi-modal process was realized.Results show that fault variables and their feature vectors can be separa-ted and detected by using the reconstructed contribution graph method,and the residual pollution problem can be avoided effectively,and the fault location accuracy is good.
关键词
凝汽器/多模态过程/故障特征提取/重构主成分分析/故障分离
Key words
condenser/multi-modal process/fault feature extraction/reconstruction-based principal com-ponent analysis/fault isolation
维普
万方数据