<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

A Convolutional Neural Network-Based Method of Inverter Fault Diagnosis in a Ship’s DC Electrical System

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

A Convolutional Neural Network-Based Method of Inverter Fault Diagnosis in a Ship’s DC Electrical System

Online veröffentlicht: 21. Dez. 2022

Seitenbereich: 105 - 114

DOI: https://doi.org/10.2478/pomr-2022-0048

Schlüsselwörter
Multi-energy hybrid ships, Inverters, Fault diagnosis, CNN

© 2022 Guohua Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Convolutional Neural Network-Based Method of Inverter Fault Diagnosis in a Ship’s DC Electrical System

Online veröffentlicht: 21. Dez. 2022

Seitenbereich: 105 - 114

DOI: https://doi.org/10.2478/pomr-2022-0048

SchlüsselwörterMulti-energy hybrid ships, Inverters, Fault diagnosis, CNN

© 2022 Guohua Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multi-energy hybrid ships, Inverters, Fault diagnosis, CNN