Deeppipe: Operating Condition Recognition of Multiproduct Pipeline Based on KPCA-CNN
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 13, Issue 2
Abstract
Operational monitoring of pipelines can prevent environmental and economic losses. However, pipeline data have the characteristics of high dimension and nonlinear coupling, which makes it difficult to determine the relationship between the data and process, resulting in a high rate of misjudgment of the operating condition. To address this issue, an operating condition recognition model based on kernel principal component analysis (KPCA)-convolutional neural network (CNN) is proposed. Deeppipe refers to the use of deep learning algorithms to solve pipeline-related problems. Considering the spatial and time-series characteristics of the pipeline, the inlet and outlet pressure matrixes of the initial station, intermediate station, and terminal station are constructed. Subsequently, the features of the pressure matrix in the time domain, frequency domain, and energy domain are extracted. KPCA is employed to obtain the reconstructed feature matrix, which is used as the input of the proposed CNN recognition model. Taking two multiproduct pipelines as examples, the effectiveness of the KPCA-CNN recognition model is verified while compared with traditional nonlinear classification models (e.g., artificial neural network, decision tree, random forest, and others). The results show that the proposed model has the highest accuracy, precision, recall, and F1 score, and all reach 100%, which has a certain guiding significance for the monitoring and management of onsite pipelines.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was part of the Program of Study on Optimization and Supply-Side Reliability of Oil Product Supply Chain Logistics System, funded under the National Natural Science Foundation of China (Grant No. 51874325). The authors are grateful to all study participants.
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Journal of Pipeline Systems Engineering and Practice
Volume 13 • Issue 2 • May 2022
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© 2022 American Society of Civil Engineers.
History
Received: Jul 6, 2021
Accepted: Dec 22, 2021
Published online: Feb 7, 2022
Published in print: May 1, 2022
Discussion open until: Jul 7, 2022
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