基于遗传算法优化单类支持向量机的 油田离心泵注水站异常检测.

At present, most of the oilfield centrifugal pump injection stations use traditional manual inspection and other methods for anomaly detection, which wastes a lot of resources and has low detection accuracy. To address this problem, a genetic algorithm optimized one-class support vector machine (GA-OC-SVM) based anomaly detection method was proposed for water injection stations. Firstly, the data of water injection station were standardized, normalized and feature extracted. Secondly, genetic algorithm was used to find the best individual value of the population as a parameter of single class support vector machine, and the detection model was established. Finally, GA-OC-SVM algorithm was compared with isolated forest algorithm, local outlier factor algorithm and other mainstream methods for anomaly detection of test data sets, and the accuracy of the algorithm was analyzed. The receiver operating characteristics (ROC) curve was used for model evaluation. The results show that the proposed GA-OC-SVM algorithm is superior, with a detection accuracy of 99%, and can save a lot of human and material resources at the same time. [ABSTRACT FROM AUTHOR]