A Hierarchical Multi-Source Condition Monitoring and Fault Diagnosis Framework for LNG Submersible Centrifugal Pumps in Marine Energy Transportation Systems
Jul 2026· Journal of Marine Science and Engineering· Vol 14, pp. 1262· 0 citations· 46 references
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.
Temperature sensor systems are critical for nuclear power plant (NPP) condition monitoring, whose reliability underpins unit safety and stability. Fault localization and diagnosis are essential to sustain their stable service. Conventional Principal Component Analysis (PCA) and Graph Neural Network (GNN) methods suffer clear drawbacks: PCA is vulnerable to noise and cannot classify fault types accurately, while GNNs struggle to quantify correlations among temperature data. This paper fuses PCA’s anomaly representation capability and GNN’s structural feature extraction capacity to propose an Abnormal Feature-GCN method for joint fault localization and diagnosis. First, an Adaptive PCA (APCA) model fed with multi-dimensional sensor features computes abnormal features. These features are then transformed into edge weights to construct a weighted graph. A dual-branch GCN is finally trained via a joint loss function for parallel multi-task learning to simultaneously locate faulty sensors and identify fault types. Validated on a nuclear primary circuit temperature sensor system under constant-, rising-, and falling-temperature working conditions, the proposed method realizes accurate fault localization and classification. The mean overall accuracy of the proposed method surpasses mainstream baselines by 2.23%, 1.62%, and 1.89% for the three typical working conditions, respectively.
Accurate fault diagnosis of pump control valves is essential for ensuring the safe and reliable operation of water supply systems. However, heterogeneous sensor signals collected from industrial monitoring systems are typically sampled at different frequencies, making effective information fusion and fault feature extraction challenging. To address these issues, this paper proposes a fault diagnosis method based on Multi-Frequency Differential Transformation (MDFT) and a Residual Attention Convolutional Network (RACNet). First, multi-source sensor signals are grouped according to their sampling frequencies and transformed into RGB trajectory images through MDFT, enabling effective fusion of heterogeneous data while preserving frequency-specific characteristics. Then, RACNet integrates dilated convolution, Efficient Channel Attention (ECA), and Global Response Normalization (GRN) to enhance feature extraction and improve the identification of subtle fault patterns. Experiments are conducted on a real-world pump control valve dataset containing four health conditions: healthy, slight lag, severe lag, and approaching failure. The proposed method achieves an average diagnostic accuracy of 97.73%, outperforming several representative fault diagnosis approaches. Ablation studies further demonstrate the effectiveness of the MDFT encoding strategy and the RACNet architecture. The results indicate that the proposed method provides an effective solution for intelligent health monitoring and fault diagnosis of pump control valves.
Changhao Song, Jingyun Zhang, Jinhui Li et al.· 2026 IEEE 27th China Confere...· 0 citations
Reliable fault diagnosis of centrifugal pumps is challenging due to the nonstationary nature of vibration signals, weak early-stage laboratory fault signatures, and overlapping characteristics among different mechanical defects. This study proposes a wavelet coherence-aware multi-branch deep ensemble framework that integrates physically meaningful time-frequency coupling with complementary deep feature learning. Multi-channel vibration signals are transformed into two-dimensional wavelet coherence maps to emphasize localized inter-sensor phase-consistent structures induced by mechanical processes. Three lightweight and architecturally diverse convolutional neural networks are trained in parallel to extract fine-scale, global, and compact structural features. Their outputs are fused through a probabilistic soft-voting strategy to improve robustness and decision stability. The framework is evaluated on vibration datasets collected from a PMT-4008 centrifugal pump test bench under three operating pressures (3.0, 3.5, and 4.0 bar). The results demonstrate consistent and reliable fault discrimination across all investigated conditions, with strong class separability confirmed by Receiver Operating Characteristic analysis and feature-space visualization. These findings demonstrate the effectiveness of the proposed framework for centrifugal pump fault diagnosis within the investigated experimental setup and operating conditions.
F. Saleem, Muhammad Umar, Jong-Myon Kim· Scientific Reports· 0 citations
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
Experimental validation on the Harbin Institute of Technology aero-engine inter-shaft bearing dataset shows that the proposed model achieves 97% diagnostic accuracy under extreme noise conditions (SNR = -5 dB).
Yang Wang, Boliang Zhang· Scientific Reports· 0 citations
Driven by global clean energy strategies, wind power develops rapidly. Bearings, core wind turbine transmission parts, govern system reliability and safety. Conventional diagnosis suffers three key practical limitations: single-sensor signals cannot fully characterize nonlinear composite faults; mainstream deep learning models act as opaque black boxes without clear diagnostic interpretability; highly coupled composite fault features cannot be separately extracted by existing algorithms. To address these challenges, this paper proposes an interpretable multi-sensor bearing nonlinear composite fault diagnosis method for wind power systems. Firstly, a multi-sensor dynamic frequency guided synchronous compressed wavelet transform is designed to precisely extract and unify multi-sensor non-stationary signal features via dynamic frequency matching, adaptive wavelet basis selection, and scale parameter optimization. Secondly, a dynamic calibration and feature enhancement network is constructed, including a dynamic dual-branch calibration fusion module for adaptive feature weighting and decoupling, and a wavelet attention feature enhancement network for sensitive feature enhancement and interpretability improvement. Finally, a Mahalanobis distance aware Krylov Transformer network is developed, integrating Mahalanobis distance to enhance early subtle fault sensitivity and an efficient global enhanced Krylov Transformer for deep feature modeling. Experiments across the three datasets yield average diagnostic accuracies of 98.80, 99.24, and 99.93%, respectively. Even under −4 dB noise interference, the proposed model retains an average accuracy above 90%. Component decoupling verification reveals that 96.0% of composite fault samples can be simultaneously identified via two independent fault channels, with the Pearson correlation coefficient between channel outputs as low as 0.18. Moreover, controlled sub-band masking tests show that masking the HH sub-band containing fault impulse information reduces the overall diagnostic accuracy from 98.45 to 76.89%, corresponding to a 21.56 percentage point drop, this sufficiently proves that the model’s inference relies heavily on high-frequency time–frequency features corresponding to fault impulses.
Sen Li, Xiao-qiang Zhao, Jie Cao et al.· Structural Health Monitoring· 0 citations
