Abstract
The fault detection of manned submersibles plays a very important role in protecting the safety of submersible equipment and personnel. However, the diving sensor data is scarce and high-dimensional, so this paper proposes a submersible fault detection method, which is made up of feature selection module based on hierarchical clustering and Autoencoder (AE), the improved Deep Convolutional Generative Adversarial Networks (DCGAN)-based data augmentation module and fault detection module using Convolutional Neural Network (CNN) with LeNet-5 structure. First, feature selection is developed to select the features that have a strong correlation with failure event. Second, data augmentation model is conducted to generate sufficient data for training the CNN model, including rough data generation and data refiners. Finally, a fault detection framework with LeNet-5 is trained and fine-tuned by synthetic data, and tested using real data. Experiment results based on sensor data from submersible hydraulic system demonstrate that our proposed method can successfully detect the fault samples. The detection accuracy of proposed method can reach 97% and our method significantly outperforms other classic detection algorithms.
Highlights
As one of the frontiers of current ocean development, deep-sea manned submersibles represent a country’s comprehensive scientific and technological strength in materials, control and marine disciplines [1]
We propose a novel high-dimensional and small-sample submersible fault detection method, which applies hierarchical clustering and AEs to select significant features and use Generative Adversarial Networks (GAN) to synthesize data
Normal samples are used for hierarchical clustering and the training of AEs in feature subsets evaluation and samples are used as predicting dataset for AEs, half of which are from normal dataset and the other half are part of fault samples
Summary
As one of the frontiers of current ocean development, deep-sea manned submersibles represent a country’s comprehensive scientific and technological strength in materials, control and marine disciplines [1]. As China’s first self-designed and self-developed operational deep-sea manned submersible, Jiaolong has performed many deep-sea dive missions and completed scientific investigations in the fields of marine geology, marine biology, and marine environment [2,3]. The fault detection of deep-sea manned submersibles has become one of the most significant tasks during the execution of the dive mission due to the person safety threat and economic loss caused by downtime of submersibles [4,5]. With the improvement of computing power and the development of signal processing technology, many researchers have made great achievements in the field of fault detection [6,7,8].
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