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


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.


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


Vol. 29 No. 4 (2022)
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  • Guohua Yan

    Merchant Marine College, Shanghai Maritime University, China
  • Yihuai Hu

    Merchant Marine College, Shanghai Maritime University, China
  • Qingguo Shi

    Merchant Marine College, Shanghai Maritime University, China

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