Work

A Data-Driven Fault Diagnosis Method for Marine Steam Turbine Condensate System Based on Deep Transfer Learning

Year: 2025

Type: article

Abstract: Accurate fault diagnosis in marine steam turbine condensate systems is challenged by insufficient real fault samples and dynamic operational conditions. To address this limitation, DTL-DFD, a novel framework integrating digital twins (DTs) and deep transfer learning (DTL), is proposed, wherein a high-fidelity physics-constrained digital twin model is constructed through the systematic injection of six diagnostic classes (1 normal + 5 faults), including insufficient circulation water flow.Through an innovative all-layer parameter initialization with a partial fine-tuning (ALPT-PF) strategy, all weights and biases from a pre-trained one-dimensional convolutional neural network (1D-CNN) were fully transferred to the target model, which was subsequently fine-tuned via a hierarchical learning rate mechanism to adapt to real-world distribution discrepancies. Experimental results demonstrate 94.34% accuracy on cross-distribution test sets with a 4.72% improvement over state-of-the-art methods, confirming significant enhancements in generalization capability and diagnostic stability under small-sample conditions with significant real data reduction, thereby providing an effective solution for the intelligent operation and maintenance of marine steam turbine systems.

Source: Machines

DOI: 10.3390/machines13080708

Authors: Yuhui Liu, Liping Chen, Duansen Shangguan, Chengcheng Yu

Institution: Huazhong University of Science and Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896