A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

doi:10.1109/JAS.2023.123180

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	A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection
	Tong Sun; Chuang Wang; Hongli Dong; Yina Zhou; Chuang Guan
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2023
卷号	10 期号:4 页码:1064-1076
摘要	Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation. Recently, deep learning (DL) has emerged as a promising tool for pipeline leakage detection (PLD). However, most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data. On the other hand, the initial parameter selection in the detection model is generally random, which may lead to unstable recognition performance. For this reason, a hybrid DL framework referred to as parameter-optimized recurrent attention network (PRAN) is presented in this paper to improve the accuracy of PLD. First, a parameter-optimized long short-term memory (LSTM) network is introduced to extract effective and robust features, which exploits a particle swarm optimization (PSO) algorithm with cross-entropy fitness function to search for globally optimal parameters. With this framework, the learning representation capability of the model is improved and the convergence rate is accelerated. Moreover, an anomaly-attention mechanism (AM) is proposed to discover class discriminative information by weighting the hidden states, which contributes to amplifying the normal-abnormal distinguishable discrepancy, further improving the accuracy of PLD. After that, the proposed PRAN not only implements the adaptive optimization of network parameters, but also enlarges the contribution of normal-abnormal discrepancy, thereby overcoming the drawbacks of instability and poor generalization. Finally, the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.
关键词	Anomaly-attention mechanism (AM) long short-term memory (LSTM) parameter-optimized recurrent attention network (PRAN) particle swarm optimization (PSO) pipeline leakage detection (PLD)
DOI	10.1109/JAS.2023.123180
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51460
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Tong Sun,Chuang Wang,Hongli Dong,et al. A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(4):1064-1076.
APA	Tong Sun,Chuang Wang,Hongli Dong,Yina Zhou,&Chuang Guan.(2023).A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection.IEEE/CAA Journal of Automatica Sinica,10(4),1064-1076.
MLA	Tong Sun,et al."A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection".IEEE/CAA Journal of Automatica Sinica 10.4(2023):1064-1076.

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