Abstract
Liquefied natural gas (LNG) submersible centrifugal pumps are critical components in marine energy transportation systems, and fault-induced degradation may threaten operational safety, transfer reliability, and maintenance efficiency. However, condition monitoring and fault diagnosis often rely on heterogeneous multi-source data, where redundant information, unequal channel sensitivity, and inter-signal coupling may obscure discriminative fault features. To address this challenge, this paper proposes a hierarchical multi-source condition monitoring and fault diagnosis framework for LNG submersible centrifugal pumps by integrating an Entropy-Weighted Sensor Selection Method (EWSSM) with a hybrid convolutional neural network (CNN)–Transformer model. Functional information is used for front-end abnormality screening, while selected response signals are used for fault category recognition. EWSSM evaluates channel contribution and suppresses redundant inputs to construct a compact fault-sensitive input space. The CNN–Transformer model combines local feature extraction with global dependency modeling to identify complex fault patterns. A laboratory-scale fault simulation platform was established, and vibration, acoustic, internal pressure-pulsation-related response information, and operating parameter data were collected under ten operating states. Experimental results show that the proposed framework achieves effective abnormality screening and accurate fault diagnosis, with an average classification accuracy of 98.73% over repeated experiments. Covariance-difference analysis further provides interpretable evidence for condition assessment by revealing fault-related multi-source response redistribution. The proposed framework provides an effective, intelligent monitoring and diagnosis solution for LNG submersible centrifugal pumps and supports reliability-oriented operation and maintenance of marine energy transportation equipment.