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

Abstract

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 on a multi-channel full convolutional neural network (MC-FCNN) is introduced to establish patterns between AUV states and potential fault types using multi-sensor signals. Firstly, the AUV raw dataset undergoes random forest multiple imputation by chained equations (RF-MICE) to serve as the input of the convolution neural network. Next, signal features are extracted through the full convolution channel, which can be fused as multilayer perceptron (MLP) input and Softmax classifier for fault identification. Finally, to validate the effectiveness of the proposed MC-FCNN model, fault diagnosis experiments are conducted using the dataset sourced from the Zhejiang University Laboratory with missing data. The experimental results demonstrate that, even with 60% of the data missing, the proposed RF-MICE with MC-FCNN model can still achieve an ideal fault identification.