Machine learning-based recognition for recognizing operating conditions of multi-product pipelines
In order to solve the problems that some operating conditions could not be automatically identified and the accuracy of abnormal operating condition recognition was low in the process of monitoring the production and operation of multi-product pipeline system,the intelligent operating condition recognition method was applied to construct a multi-product pipeline operating condition recognition model with real-time monitoring capability.First,logic rule discrimination methods and event logs in the multi-product pipeline system were used to supplement the data labels.Second,the data were segmented according to the start and end time of the operating conditions,and the subsequence of different operating conditions were extracted by using the sliding window.Third,the features of subsequence were extracted to construct the model for operating condition recognition of multi-product pipelines,and the recognition effects of six classification models,namely,random forest(RF),adaptive boosting(AdaBoost),support vector machine(SVM),time series forest(TSF),random interval spectral forest(RISF)and sequence learner(SEQL),were compared and analyzed.Finally,a real multi-product pipeline was used as an example for model validation.The results show that the TSF model has the highest recognition accuracy for the four operating conditions of valve switching,valve internal leakage,pigging and sling pump,and is more suitable for the recognition of short-term operating conditions.In contrast,the recognition precision of the AdaBoost model has a higher probability of including the true value in the 95%confidence interval.
machine learningmulti-product pipelineoperating conditionsoperating condition recognitionclassification model
万方数据
维普
