Research On Fault Diagnosis Method Of Oil Pipeline Leakage Based On Intelligent Optimization Algorithm

Energy plays a pivotal role in national economic development,and pipelines are one of the important ways to ensure energy transportation.Different degrees of damage caused by external forces,pipeline equipment aging,natural corrosion and other factors will lead to the deterioration of pipeline operation conditions year by year,and the safety hazards have become increasingly prominent.Once a pipeline leakage failure occurs,if it is not dealt with in time or repaired improperly,it will seriously affect the production operation and the safety of the people.Therefore,on the premise of ensuring the safe operation of pipelines,using scientific methods to diagnose pipeline leakage faults is one of the problems that need to be solved urgently,and it has important social and economic significance.Based on the collation and analysis of domestic and foreign literature on fault diagnosis,this study analyzes the commonly used pipeline leakage detection technologies and compares the comprehensive performance of each method,and clarifies the generation mechanism of vibration signals and the principle of fault diagnosis based on acoustic signals.Provide a theoretical basis for subsequent research;use the improved empirical mode decomposition algorithm to extract fault features,and aim at the endpoint effect and mode aliasing defects of the traditional EMD algorithm itself,respectively introduce the mirror continuation method and Gaussian white noise to improve it,Combining the correlation coefficient method and time-frequency analysis to conduct feature extraction experiments,the simplified and optimized fault feature vectors were obtained;a pipeline leakage fault diagnosis model based on IWOA-SVM was constructed,from the three aspects of population initialization,convergence factor nonlinearization and inertia weight The traditional WOA is improved to enhance the optimization ability of WOA,the kernel parameters and penalty factors in the SVM model are optimized by using IWOA,and the IWOA-SVM model is established for pipeline fault diagnosis.Diagnostic accuracy;a multi-working-condition fault diagnosis model based on the intelligent optimization algorithm EEMD-ICNN is established.From the perspective of deep-level feature learning,the fault characteristic parameters extracted after decomposition by the EEMD algorithm are used as the input of the ICNN network,and the fault type is used as the expected Based on the output of the network,a network training method and network structure suitable for multi-working condition fault diagnosis are constructed,and compared with the IWOA-SVM diagnosis model through experiments,the results show that the EEMD-ICNN model has better diagnostic effect and diagnostic accuracy,which can better reflect the fault type of the oil pipeline.The oil pipeline leakage fault diagnosis model constructed in this paper has good diagnostic accuracy and generalization ability,which can provide a certain reference for future research on pipeline fault diagnosis.However,due to the limitations of academic ability and time,the research still has limitations.Exploring a more complete fault sample index system can be the focus of follow-up research.