A Study on Deep Learning-Based Fault Diagnosis and Classification for Marine Engine System Auxiliary Equipment

Kim, Jeong-yeong; Lee, Tae-hyun; Lee, Songho; Lee, Jong-jik; Lee, Won-kyun; Park, Jong-won

doi:10.3390/pr10071345

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…Deep learning, as a pivotal manifestation of data-driven approaches, exhibits heightened diagnostic prowess when confronted with data characterized by intricate features and high variability. It has garnered outstanding outcomes in the realm of fault diagnosis research [5][6][7][8][9]. However, the efficacy of deep diagnostic models is contingent upon access to an extensive dataset comprising a substantial number of accurately labeled training samples.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Marine Engine Cross Working Conditions Based on Transfer Learning

Wang,

Cao,

Cui

et al. 2024

JMSE

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Marine engines confront challenges of varying working conditions and intricate failures. Existing studies have primarily concentrated on fault diagnosis in a single condition, overlooking the adaptability of these methods in diverse working condition. To address the aforementioned issues, we propose a cross working condition fault diagnosis method named the Balanced Adaptation Domain Weighted Adversarial Network (BADWAN). This method combines transfer learning to tackle the challenges of cross working condition diagnosis with limited labels. Specifically tailored for scenarios with incomplete labeling in the target working conditions, we designed an Enhanced Centroid Balance scheme to balance the label space, thereby enhancing the model’s transfer capabilities. Additionally, we designed an Instance Affinity Weighting scheme on the foundation of Class-level Weighting, refining the model to the instance level for effective information interaction. Furthermore, we incorporated the Adaptive Uncertainty Suppression strategy to further boost the model’s classification prowess. Two experimental scenarios were designed to demonstrate the effectiveness of the proposed model using a Wärtsilä9L34DF dual-fuel engine as an experimental subject. The results demonstrate an over 90% diagnostic accuracy in scenarios with complete target working condition labels and 86% accuracy in scenarios with incomplete labels, outperforming other transfer learning models. The BADWAN model excels in cross-condition fault diagnosis tasks for marine engines with incomplete target working condition labels, offering a novel solution to this field.

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Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Marine Engine Cross Working Conditions Based on Transfer Learning

Wang,

Cao,

Cui

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Han et al [6] introduced CNN for detecting and isolating propeller faults in dynamically positioned marine vessels. Kim et al [7] utilized a one-dimensional CNN model to analyze the vibration data of ship auxiliary equipment for fault diagnosis purposes. Ftoutou et al [8] used an unsupervised fuzzy clustering approach for the time-frequency signal analysis of diesel engines, and the algorithm was experimentally proved to have a high fault detection rate.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Marine Diesel Engines under Partial Set and Cross Working Conditions Based on Transfer Learning

Guo,

Zhang

2023

JMSE

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The development of intelligent ships has an urgent demand for intelligent fault diagnosis technology. The working conditions and fault modes of high-power marine diesel engines gradually tend to be diversified and complicated, and the problems of reliability and safety are becoming more and more prominent. There are a lot of working condition data that lack fault labels, and the fault modes are asymmetric among different working conditions, so it is urgent to study effective fault diagnosis methods. Taking a marine diesel engine as the case validation object, we set up cross-condition and partial set fault diagnosis scenarios, proposed transferring knowledge from the source condition to the target condition for the problem of the lack of fault labels in the target condition, and designed a multi-scale and multi-view domain adversarial network (MMDAN) method for experimental validation using 6S50MC-C7 marine diesel engine system operation data. According to the experimental results, the average diagnostic accuracy of this method reached 96.58%, with a short processing time. Furthermore, it exhibits superior diagnostic performance compared to other transfer learning models in the cross-condition partial set transfer task. Additionally, the method proposed in this paper also offers a new approach and reference for the intelligent diagnosis of other equipment in ships.

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A self-supervised contrastive learning framework with the nearest neighbors matching for the fault diagnosis of marine machinery

2023

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A Study on Deep Learning-Based Fault Diagnosis and Classification for Marine Engine System Auxiliary Equipment

Cited by 12 publications

References 17 publications

A Fault Diagnosis Method for Marine Engine Cross Working Conditions Based on Transfer Learning

A Fault Diagnosis Method for Marine Engine Cross Working Conditions Based on Transfer Learning

Fault Diagnosis of Marine Diesel Engines under Partial Set and Cross Working Conditions Based on Transfer Learning

A self-supervised contrastive learning framework with the nearest neighbors matching for the fault diagnosis of marine machinery

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