دورية أكاديمية
High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN
| العنوان: | High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN |
|---|---|
| المؤلفون: | Jia-Ling Xie, Wei-Feng Shi, Ting Xue, Yu-Hang Liu |
| المصدر: | Journal of Marine Science and Engineering, Vol 12, Iss 4, p 583 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Naval architecture. Shipbuilding. Marine engineering LCC:Oceanography |
| مصطلحات موضوعية: | ship electric propulsion system, fault diagnosis, high-resistance connection (HRC), residual network (ResNet), bidirectional long short-term memory (BiLSTM), Naval architecture. Shipbuilding. Marine engineering, VM1-989, Oceanography, GC1-1581 |
| الوصف: | The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing an accurate model of the complex system, and it is easy to cause false alarms. Data-driven methods, such as deep learning, can automatically learn from the mass of data, extract and analyze fault characteristics, and create a more objective distinction system state. A deep learning fault diagnosis model based on ResNet feature extraction capability and bidirectional long-term memory network timing processing capability is proposed to realize fault diagnosis of high resistance connections in ship electric propulsion systems. The results show that the res-convolutional BiLSTM deep neural network (Res-CBDNN) can fully integrate the advantages of the two networks, efficiently process fault current data, and achieve high-performance fault diagnosis. The accuracy of Res-CBDNN can be kept above 85% in a noisy environment, and it can effectively monitor the high resistance connection fault of ship electric propulsion systems. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2077-1312 |
| Relation: | https://www.mdpi.com/2077-1312/12/4/583; https://doaj.org/toc/2077-1312 |
| DOI: | 10.3390/jmse12040583 |
| URL الوصول: | https://doaj.org/article/3abd4bff4c6642cba6c327d2189ee4a5 |
| رقم الأكسشن: | edsdoj.3abd4bff4c6642cba6c327d2189ee4a5 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20771312 |
|---|---|
| DOI: | 10.3390/jmse12040583 |