School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
China National Deep Sea Center, Qingdao 266237, China.
Sensors (Basel). 2021 Dec 29;22(1):204. doi: 10.3390/s22010204.
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.
载人潜水器的故障检测对于保护潜水器设备和人员的安全起着非常重要的作用。然而，潜水传感器数据是稀缺和高维的，因此本文提出了一种基于分层聚类和自动编码器（AE）的特征选择模块、基于改进的深度卷积生成对抗网络（DCGAN）的数据增强模块和使用 LeNet-5 结构的卷积神经网络（CNN）的故障检测模块组成的潜水器故障检测方法。首先，开发特征选择来选择与故障事件具有强相关性的特征。其次，进行数据增强模型以生成足够的训练 CNN 模型的数据，包括粗糙数据生成和数据细化器。最后，通过合成数据训练和微调具有 LeNet-5 的故障检测框架，并使用真实数据进行测试。基于潜水器液压系统传感器数据的实验结果表明，我们提出的方法可以成功检测故障样本。所提出方法的检测准确率可达 97%，明显优于其他经典检测算法。
