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.
关键词
剩余寿命预测/腐蚀管道/核主成分分析/鸽群优化算法/极限学习机
Key words
remaining life prediction/corrosion pipeline/kernel principal component analysis/pigeon col-ony optimization algorithm/extreme learning machine
维普
万方数据