Application of Improved SSA-KELM Model in Remaining Life Prediction of Buried Corrosion Pipeline

In order to improve the residual life prediction accuracy of buried corrosion pipelines, a residual life prediction model wasconstructed.AresiduallifepredictionmodelofKernelExtremeLearningMachine (KELM) basedon Kernel Principal Component Analysis (KPCA) and Improved Sparrow Search Algorithm (ISSA) was established.Firstly, KPCA was used to preprocess the original data, and the main feature vectors of buried corrosion pipelines were extracted and the evaluation indicators were reconstructed. Secondly, in view of the defects of SSA easily falling into local optimum and reducing the anti-stagnation performance in the later iteration, an improved SSA scheme was proposed: using Tent chaos to improve its ergodicity; introduce adaptive security value to adjust the sparrow search area; using Gaussian disturbance to focus on searching near the optimal solution region to improve the global optimization capability of SSA. The kernel parameters and penalty coefficients in KELM were optimized by ISSA again, and the KPCA-ISSA-KELM buried corrosion pipeline residual life prediction model was finally constructed. Taking a buried pipeline as an example, the simulation results show that the mean square error, mean absolute error value and coefficient of determination of the prediction results of the KPCA-ISSAKELM model is 0.249, 0.096, and 0.998, respectively, which are better than other models.It is proved that KPCA-ISSAKELM's residual life prediction model of buried corrosion pipeline has strong robustness, which can provide an important reference for pipeline system research.