Publication detail

Deeppipe: Operating Condition Recognition of Multiproduct Pipeline Based on KPCA-CNN

Wang, Chang Zheng, Jianqin Liang, Yongtu Liao, Qi Wang, Bohong Zhang, Haoran

Original Title

Deeppipe: Operating Condition Recognition of Multiproduct Pipeline Based on KPCA-CNN

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Deeppipe; Operating Condition Recognition; Multiproduct Pipeline; KPCA-CNN

Authors

Wang, Chang; Zheng, Jianqin; Liang, Yongtu; Liao, Qi; Wang, Bohong; Zhang, Haoran

Released

1. 5. 2022

Publisher

ASCE-AMER SOC CIVIL ENGINEERS

Location

Reston

ISBN

1949-1190

Periodical

Journal of Pipeline Systems Engineering and Practice

Year of study

2

Number

13

State

United States of America

Pages from

04022006

Pages to

04022006

Pages count

11

URL

https://ascelibrary.org/doi/10.1061/%28ASCE%29PS.1949-1204.0000641

BibTex

@article{BUT182584,
  author="Wang, Chang and Zheng, Jianqin and Liang, Yongtu and Liao, Qi and Wang, Bohong and Zhang, Haoran",
  title="Deeppipe: Operating Condition Recognition of Multiproduct Pipeline Based on KPCA-CNN",
  journal="Journal of Pipeline Systems Engineering and Practice",
  year="2022",
  volume="2",
  number="13",
  pages="11",
  doi="10.1061/(ASCE)PS.1949-1204.0000641",
  issn="1949-1190",
  url="https://ascelibrary.org/doi/10.1061/%28ASCE%29PS.1949-1204.0000641"
}