Real-Time Fault Diagnosis of Mooring Chain Jack Hydraulic System Based on Multi-Scale Feature Fusion Under Diverse Operating Conditions DOI Creative Commons
Yujia Liu, Wenhua Li, Haoran Ye

et al.

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(4), P. 783 - 783

Published: April 15, 2025

The condition monitoring of mooring equipment is an important engineering reliability issue during the operation a floating production storage and offloading unit (FPSO). chain jack (CJ) key for powering in spread system. Under complex dynamic marine operating conditions, different severity faults CJ hydraulic system display distinct time-scale characteristics. Hence, this paper proposes real-time fault diagnosis method based on multi-scale feature fusion. Firstly, model incorporates convolutional neural network (CNN) layer to extract localized spatial features from multivariate time-series data, effectively identifying patterns over associated short intervals. Subsequently, bidirectional long short-term memory (BiLSTM) introduced construct temporal comprehensively capture evolution severity. Finally, global attention mechanism (GAM) emphasizes persistent behaviors across time scales, dynamically prioritizing relevant improve diagnostic accuracy interpretability. study results indicate that proposed model’s improves by 7.36% CNN-GAM 11 failure modes, up 99.34%. This contributes safe FPSO guiding operations under load conditions.

Language: Английский

Real-Time Fault Diagnosis of Mooring Chain Jack Hydraulic System Based on Multi-Scale Feature Fusion Under Diverse Operating Conditions DOI Creative Commons
Yujia Liu, Wenhua Li, Haoran Ye

et al.

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(4), P. 783 - 783

Published: April 15, 2025

The condition monitoring of mooring equipment is an important engineering reliability issue during the operation a floating production storage and offloading unit (FPSO). chain jack (CJ) key for powering in spread system. Under complex dynamic marine operating conditions, different severity faults CJ hydraulic system display distinct time-scale characteristics. Hence, this paper proposes real-time fault diagnosis method based on multi-scale feature fusion. Firstly, model incorporates convolutional neural network (CNN) layer to extract localized spatial features from multivariate time-series data, effectively identifying patterns over associated short intervals. Subsequently, bidirectional long short-term memory (BiLSTM) introduced construct temporal comprehensively capture evolution severity. Finally, global attention mechanism (GAM) emphasizes persistent behaviors across time scales, dynamically prioritizing relevant improve diagnostic accuracy interpretability. study results indicate that proposed model’s improves by 7.36% CNN-GAM 11 failure modes, up 99.34%. This contributes safe FPSO guiding operations under load conditions.

Language: Английский

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