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