A Hybrid Model Integrating CNN–BiLSTM and CBAM for Anchor Damage Events Recognition of Submarine Cables

Xu, Chunying; Liang, Ruixin; Wu, Xinjie; Cao, Chen; Chen, Jiawang; Yang, Chengyu; Zhou, Yu; Wen, Tsui-Jung; Lv, Hexin; Wei, Chunbo

doi:10.1109/tim.2023.3290323

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…And then, the fault diagnosis of hydraulic components is realized. The model parameter setting has a great influence on the network performance and the diagnostic accuracy [ 18 , 19 , 20 ]. We aim to address the problems of insufficient search accuracy and the imbalance between global searches and local explorations in the tuna swarm optimization algorithm (TSO) when solving high-dimensional complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM

Zhang,

Zheng,

Liu

et al. 2024

Sensors

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In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

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Section: Introductionmentioning

confidence: 99%