Prediction and analysis of pipeline remaining life based on KPCA-PIO-ELM model
In order to improve the prediction accuracy of remaining life of corroded pipelines,an Extreme Learning Machine(ELM)prediction model based on Kernel Principal Component Analysis(KPCA)and Pigeon Colony Optimization algorithm(PIO)is proposed.The key corrosion factors are extracted by KPCA to reduce the dimension of prediction index.PIO is used to optimize the input weight and hidden layer threshold of ELM to improve the prediction accuracy.In order to test the efficiency of the model,50 sets of data of a water injection pipeline are taken as an example to study,and compared with ELM and BP mod-els.The results show that MAE,MAPE and RMSE of the model are better than the comparison model,which proves that KPCA-PIO-ELM model is feasible and obviously superior in predicting the remaining life of water injection pipeline.
remaining life predictioncorrosion pipelinekernel principal component analysispigeon col-ony optimization algorithmextreme learning machine
万方数据
维普
