Research on Corrosion Degree of Heating Pipeline Based on Hybrid Multi Class Feature Selection
In recent years,there has been energy waste caused by insufficient heating due to corrosion of heating pipes in northern industrial plants.In order to achieve more accurate corrosion prediction of heating pipes,a corro-sion degree prediction method using tapping method to collect sound signals of heating pipes,extract screening fea-tures,and combine pattern recognition has been proposed.An improved ReliefFP-NMSVMRFE-SVM algorithm is pro-posed to address the issues of classical ReliefF algorithm not considering redundancy between features,SVMRFE al-gorithm being able to only handle binary classification,and slow running speed.In the early stage,Pearson correlation coefficients were used to remove redundant features from the feature set processed by the ReliefF algorithm.Then,a second screening was performed in the improved MSVMRFE linear and nonlinear algorithms to obtain a set of score decreasing sorted feature subsets,which were then combined with classifiers BP and SVM to classify and predict the feature subsets.The results show that the ReliefFP-NSVMRFE-SVM algorithm has the highest recognition accuracy and short time,with a prediction result of 99.85%on the training set and 97.14%on the independent test set.It has certain applicability for detecting the degree of internal corrosion in heating pipelines.
prediction of corrosion degreeknocking methodfeature selectionReliefF algorithmMSVMRFE algorithm
万方数据
维普
