基于机器学习的成品油管道运行工况识别

Machine learning-based recognition for recognizing operating conditions of multi-product pipelines

为改善成品油管道系统生产运行监测过程中不能自动识别部分运行状态,以及异常工况识别准确率较低的问题,应用智能工况识别方法,构建具有实时监测能力的成品油管道运行工况识别模型。首先,采用逻辑规则判别方法,并根据成品油管道系统中的事件日志补充数据标签;其次,按照工况的起止时间对数据进行分段,并采用滑动窗口的方式提取不同工况的子序列及其特征;然后构建成品油管道运行工况识别模型,并与随机森林(RF)、自适应提升(AdaBoost)、支持向量机(SVM)、时间序列森林(TSF)、随机区间谱系森林(RISF)和序列学习器(SEQL)等6种机器学习分类模型进行对比,分析其识别效果;最后,以某真实成品油管道为例,进行模型验证。结果表明:TSF模型对阀门开关、阀门内漏、清管和甩泵4种工况的识别精确度最高,且更适合短期内运行工况的识别;而AdaBoost模型的识别精确度在95%的置信区间内所含真实值的概率更高。

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.