Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments
ObjectivesTo address the performance degradation in fault diagnosis of rotating machinery caused by noise interference in practical applications, a novel fault diagnosis approach based on Mel-frequency cepstral coefficients (MFCC) and a parallel dual-channel convolutional neural network (PDCNN) is p...
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| Format: | Article |
| Language: | English |
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Editorial Office of Chinese Journal of Ship Research
2025-04-01
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| Series: | Zhongguo Jianchuan Yanjiu |
| Subjects: | |
| Online Access: | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03814 |
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| _version_ | 1850141120524189696 |
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| author | Qianming SHANG Wanying JIANG Yi ZHOU Zhengqiang WANG Yubo SUN |
| author_facet | Qianming SHANG Wanying JIANG Yi ZHOU Zhengqiang WANG Yubo SUN |
| author_sort | Qianming SHANG |
| collection | DOAJ |
| description | ObjectivesTo address the performance degradation in fault diagnosis of rotating machinery caused by noise interference in practical applications, a novel fault diagnosis approach based on Mel-frequency cepstral coefficients (MFCC) and a parallel dual-channel convolutional neural network (PDCNN) is proposed. This method aims to improve the quality of fault feature extraction from vibration signals and enhance fault diagnosis capabilities under noisy conditions. MethodsThe MFCC is used to extract features from vibration signals contaminated by noise. Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. Its robustness to noise and diagnostic performance significantly surpass traditional methods. ConclusionThe findings of this study can provide valuable references for gearbox fault diagnosis in environments with strong noise. |
| format | Article |
| id | doaj-art-72b4dca3f45b433f89f499741c476c2c |
| institution | OA Journals |
| issn | 1673-3185 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Editorial Office of Chinese Journal of Ship Research |
| record_format | Article |
| series | Zhongguo Jianchuan Yanjiu |
| spelling | doaj-art-72b4dca3f45b433f89f499741c476c2c2025-08-20T02:29:34ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852025-04-01202303810.19693/j.issn.1673-3185.03814ZG3814Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environmentsQianming SHANG0Wanying JIANG1Yi ZHOU2Zhengqiang WANG3Yubo SUN4School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaObjectivesTo address the performance degradation in fault diagnosis of rotating machinery caused by noise interference in practical applications, a novel fault diagnosis approach based on Mel-frequency cepstral coefficients (MFCC) and a parallel dual-channel convolutional neural network (PDCNN) is proposed. This method aims to improve the quality of fault feature extraction from vibration signals and enhance fault diagnosis capabilities under noisy conditions. MethodsThe MFCC is used to extract features from vibration signals contaminated by noise. Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. Its robustness to noise and diagnostic performance significantly surpass traditional methods. ConclusionThe findings of this study can provide valuable references for gearbox fault diagnosis in environments with strong noise.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03814electric propulsiongearboxesfailure analysisfault diagnosisfeature extractionmel-frequency cepstral coefficientsconvolutional neural networks |
| spellingShingle | Qianming SHANG Wanying JIANG Yi ZHOU Zhengqiang WANG Yubo SUN Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments Zhongguo Jianchuan Yanjiu electric propulsion gearboxes failure analysis fault diagnosis feature extraction mel-frequency cepstral coefficients convolutional neural networks |
| title | Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments |
| title_full | Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments |
| title_fullStr | Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments |
| title_full_unstemmed | Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments |
| title_short | Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments |
| title_sort | fault diagnosis of marine electric thruster gearbox based on mpdcnn under strong noisy environments |
| topic | electric propulsion gearboxes failure analysis fault diagnosis feature extraction mel-frequency cepstral coefficients convolutional neural networks |
| url | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03814 |
| work_keys_str_mv | AT qianmingshang faultdiagnosisofmarineelectricthrustergearboxbasedonmpdcnnunderstrongnoisyenvironments AT wanyingjiang faultdiagnosisofmarineelectricthrustergearboxbasedonmpdcnnunderstrongnoisyenvironments AT yizhou faultdiagnosisofmarineelectricthrustergearboxbasedonmpdcnnunderstrongnoisyenvironments AT zhengqiangwang faultdiagnosisofmarineelectricthrustergearboxbasedonmpdcnnunderstrongnoisyenvironments AT yubosun faultdiagnosisofmarineelectricthrustergearboxbasedonmpdcnnunderstrongnoisyenvironments |