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Volume 10 Issue 4
Apr.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
T. Sun, C. Wang, H. L. Dong, Y. N. Zhou, and C. Guan, “A novel parameter-optimized recurrent attention network for pipeline leakage detection,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1064–1076, Apr. 2023. doi: 10.1109/JAS.2023.123180
Citation: T. Sun, C. Wang, H. L. Dong, Y. N. Zhou, and C. Guan, “A novel parameter-optimized recurrent attention network for pipeline leakage detection,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1064–1076, Apr. 2023. doi: 10.1109/JAS.2023.123180

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

doi: 10.1109/JAS.2023.123180
Funds:  This work was supported in part by the National Natural Science Foundation of China (U21A2019, 61873058), Hainan Province Science and Technology Special Fund of China (ZDYF2022SHFZ105), and the Alexander von Humboldt Foundation of Germany
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  • 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.


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    • In this paper, we propose a novel parameter-optimized recurrent attention network (PRAN) for the pipeline leakage detection, which can not only implement the adaptive optimization of network parameters, but also enlarge the contribution of normal-abnormal discrepancy, thereby further improving the detection accuracy
    • We present a novel particle swarm optimization (PSO)-optimized long short-term memory (LSTM) network for the pipeline leakage detection. In particular, the PSO algorithm can be seamlessly incorporated into the detection model by adopting a fitness function that is consistent with the LSTM. In this way, the selection of LSTM parameters is released from the dependency on expert experience, thus improving the performance of detection model
    • We introduce an anomaly-attention mechanism (AM) to excavate class discriminative information by weighting the hidden states, which is conducive to enlarging the normal-abnormal distinguishable discrepancy. The AM enhances the contribution of abnormal time points, which is capable of overcoming the poor generalization and enhancing the representational power of model
    • The proposed PRAN algorithm is successfully applied to the pipeline leakage detection. Experimental results demonstrate that the proposed model outperforms other existing methods in terms of accuracy and convergence


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