Fault Diagnosis of Autonomous Underwater Vehicle with Missing Data Based on Multi-Channel Full Convolutional Neural Network

Fault Diagnosis of Autonomous Underwater Vehicle with Missing Data Based on Multi-Channel Full Convolutional Neural Network

The fault feature extraction and diagnosis of autonomous underwater vehicles (AUVs) in complex environments pose significant challenges due to the intricate nature of the signals that reflect the AUVs’ states in the deep ocean. In this paper, an analytical model-free fault diagnosis algorithm based...

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Bibliographic Details
Main Authors: Yunkai Wu, Aodong Wang, Yang Zhou, Zhiyu Zhu, Qingjun Zeng
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Machines
Subjects:
AUV fault diagnosis
multi-sensor signals
convolution neural network
missing data
random forest multiple imputation
Online Access:https://www.mdpi.com/2075-1702/11/10/960

Internet

https://www.mdpi.com/2075-1702/11/10/960

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