A convolutional neural network-based method of inverter fault diagnosis in a ship’s DC electrical system

• Autorzy: Yan Guohua , Hu Yihuai , Shi Qingguo

Identyfikatory

DOI

10.2478/pomr-2022-0048

• Języki publikacji: EN

Abstrakty

EN

Multi-energy hybrid ships are compatible with multiple forms of new energy, and have become one of the most important directions for future developments in this field. A propulsion inverter is an important component of a hybrid DC electrical system, and its reliability has great significance in terms of safe navigation of the ship. A fault diagnosis method based on one-dimensional convolutional neural network (CNN) is proposed that considers the mutual influence between an inverter fault and a limited ship power grid. A tiled voltage reduction method is used for one-to-one correspondence between the inverter output voltage and switching combinations, followed by a combination of a global average pooling layer and a fully connected layer to reduce the model overfitting problem. Finally, fault diagnosis is verified by a Softmax layer with good anti-interference performance and accuracy.

Słowa kluczowe

EN

multi-energy hybrid ships; inverters; fault diagnosis; CNN

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2022
• Tom: nr 4
• Strony: 105--114
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Yan Guohua

• Merchant Marine College Shanghai Maritime University China

• https://orcid.org/0000-0002-1065-5452

autor

Hu Yihuai

• Merchant Marine College Shanghai Maritime University China

• https://orcid.org/0000-0003-3000-7855

autor

Shi Qingguo

• Merchant Marine College Shanghai Maritime University China

• https://orcid.org/0000-0003-3776-8438

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).