Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines

Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating conditions of the multi-product pipeline change frequently, and the recognition and monitoring by on-site personnel are easy to cause misjudgment,...

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Veröffentlicht in:Process safety and environmental protection Jg. 150; S. 510 - 521
Hauptverfasser: Zheng, Jianqin, Du, Jian, Liang, Yongtu, Liao, Qi, Li, Zhengbing, Zhang, Haoran, Wu, Yi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Rugby Elsevier B.V 01.06.2021
Elsevier Science Ltd
Schlagworte:
Anomalies
Learning algorithms
Machine learning
Mathematical models
Model accuracy
Monitoring
Multilayers
Neural networks
Operating condition recognition
Parameters
Pipeline
Pipeline safety
Pipelines
Recognition
Risk management
Safety management
Semi-supervised learning
Sensitivity analysis
LSTM
Sensitivity analysis
AE
MRI
GMM
KS
KNN
SVM
BP
Pipeline
DT
XGB
RF
Operating condition recognition
Semi-supervised learning
SAE
GB
MLNN
SCADA
ISSN:0957-5820, 1744-3598
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Zusammenfassung:Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating conditions of the multi-product pipeline change frequently, and the recognition and monitoring by on-site personnel are easy to cause misjudgment, so the operating conditions of the pipeline cannot be accurately recognized. Noticeably, operating condition recognition is an important part of pipeline safety and risk management. Although ample operating data are stored in SCADA system, these data are lack of corresponding condition labels, making it hard to be mined. In this work, a semi-supervised learning for operating condition recognition is proposed to overcome aforementioned issues. Firstly, the operating parameters of each station are preprocessed and collected to construct into data matrices to overcome transient disturbance considering the pipeline space characteristics and time series of the operating data. Then stacked autoencoder (SAE) is used to pre-train the network parameters of multi-layer neural network (MLNN) based on a large amount of unlabeled operating data. After that, MLNN is fine-tuned based on a small amount of labeled data annotated by referring to the operation log. To verify the effectiveness of the semi-supervised learning, a real multi-product pipeline is taken as an example for operating condition recognition. The accuracy, precision, recall and F1 score is 95 %, 95 %, 80 % and 80 %, respectively. Results show that the condition recognition accuracy of the proposed model is better than other machine learning models. Finally, the sensitivity analysis is conducted to illustrate the importance of SAE in this classification model.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2021.04.031