Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer
In modern intelligent shipping, ensuring the stable and reliable technical condition of marine diesel engines is critical for safe and efficient vessel operations. Conventional fault diagnosis approaches and many existing Transformer-based methods often focus on single-scale features, potentially ov...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2348 |
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| author | Manyi Chen Huibing Gan Hangjie Wu |
| author_facet | Manyi Chen Huibing Gan Hangjie Wu |
| author_sort | Manyi Chen |
| collection | DOAJ |
| description | In modern intelligent shipping, ensuring the stable and reliable technical condition of marine diesel engines is critical for safe and efficient vessel operations. Conventional fault diagnosis approaches and many existing Transformer-based methods often focus on single-scale features, potentially overlooking subtle fault indicators and reducing diagnostic accuracy under complex working conditions. To address these limitations, this paper proposes a Multi-Scale Attention Transformer (MSAT) model that integrates both high- and low-resolution attention mechanisms. This multi-scale strategy enhances the extraction of detailed and coarse-grained features, improving the model’s capacity to detect and characterize complex diesel engine faults. Additionally, an optimized Nadam optimizer is employed to refine convergence speed and accuracy, surpassing the Adam-based baseline by 0.71%. Rigorous testing on a publicly available diesel engine fault dataset demonstrates that the MSAT model achieves a diagnostic accuracy of 99.86% at a 60 dB signal-to-noise ratio (SNR), outperforming established models such as GRU and LSTM by more than 1%. Even under severe noise interference (0 dB SNR), the model maintains a high accuracy of 96.86%, highlighting its robustness and suitability for real-time monitoring in challenging marine environments. By quantitatively validating these improvements in diagnostic accuracy and noise resistance, this work offers a novel and effective solution for predictive maintenance and operational condition assessment of marine diesel engines, contributing to the reliability and safety of intelligent shipping systems. |
| format | Article |
| id | doaj-art-9c8f8d6c43184082a066f65de7201966 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-9c8f8d6c43184082a066f65de72019662024-12-27T14:33:40ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212234810.3390/jmse12122348Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism TransformerManyi Chen0Huibing Gan1Hangjie Wu2Marine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaIn modern intelligent shipping, ensuring the stable and reliable technical condition of marine diesel engines is critical for safe and efficient vessel operations. Conventional fault diagnosis approaches and many existing Transformer-based methods often focus on single-scale features, potentially overlooking subtle fault indicators and reducing diagnostic accuracy under complex working conditions. To address these limitations, this paper proposes a Multi-Scale Attention Transformer (MSAT) model that integrates both high- and low-resolution attention mechanisms. This multi-scale strategy enhances the extraction of detailed and coarse-grained features, improving the model’s capacity to detect and characterize complex diesel engine faults. Additionally, an optimized Nadam optimizer is employed to refine convergence speed and accuracy, surpassing the Adam-based baseline by 0.71%. Rigorous testing on a publicly available diesel engine fault dataset demonstrates that the MSAT model achieves a diagnostic accuracy of 99.86% at a 60 dB signal-to-noise ratio (SNR), outperforming established models such as GRU and LSTM by more than 1%. Even under severe noise interference (0 dB SNR), the model maintains a high accuracy of 96.86%, highlighting its robustness and suitability for real-time monitoring in challenging marine environments. By quantitatively validating these improvements in diagnostic accuracy and noise resistance, this work offers a novel and effective solution for predictive maintenance and operational condition assessment of marine diesel engines, contributing to the reliability and safety of intelligent shipping systems.https://www.mdpi.com/2077-1312/12/12/2348fault diagnosismarine diesel engineintelligent shippingmulti-scale attention mechanismTransformer |
| spellingShingle | Manyi Chen Huibing Gan Hangjie Wu Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer Journal of Marine Science and Engineering fault diagnosis marine diesel engine intelligent shipping multi-scale attention mechanism Transformer |
| title | Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer |
| title_full | Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer |
| title_fullStr | Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer |
| title_full_unstemmed | Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer |
| title_short | Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer |
| title_sort | research on fault diagnosis method for marine diesel engines based on multi scale attention mechanism transformer |
| topic | fault diagnosis marine diesel engine intelligent shipping multi-scale attention mechanism Transformer |
| url | https://www.mdpi.com/2077-1312/12/12/2348 |
| work_keys_str_mv | AT manyichen researchonfaultdiagnosismethodformarinedieselenginesbasedonmultiscaleattentionmechanismtransformer AT huibinggan researchonfaultdiagnosismethodformarinedieselenginesbasedonmultiscaleattentionmechanismtransformer AT hangjiewu researchonfaultdiagnosismethodformarinedieselenginesbasedonmultiscaleattentionmechanismtransformer |