Hydrogen leakage source positioning method in deep belief network based on fully confined space Gaussian distribution model

Abstract

Hydrogen leakage has become the biggest bottleneck restricting the development of hydrogen energy. Accurately locating the leakage is conducive to improving the hydrogen energy safety technology system. In this paper, a deep belief network pre-training and fine-tuning (DBN-PF) leakage source positioning coordinate method based on the fully confined space hydrogen Gaussian distribution model is proposed. Firstly, a fully confined space hydrogen Gaussian distribution model is established, and then a pre-training dataset is generated. The Gaussian-Gaussian Restricted Boltzmann Machine (GGRBM) in Deep Belief Network (DBN) is unsupervised pre-trained layer by layer, and the back propagation (BP) neural network in DBN is supervised pre-trained. Secondly, the hydrogen leakage experiment in the fully confined space was carried out, in which 8 hydrogen concentration sensors (HCSs) were placed on the top of the fully confined space, and the leakage experiment was carried out in sequence at 25 leakage locations. Finally, the HCS experimental data of one leakage location is extracted to fine-tune DBN. The experimental data at the other leakage locations were used to verify the positioning results. The results show that the positioning results average error of the proposed method is 20.62 mm. The average error accounts for 2.97% of the diagonal length of the closed model XY plane. Compared with BP neural network trained directly with the same fine-tuning dataset, the positioning error is reduced by at least 82.37%. Compared with the BP neural network with the same pre-training and fine tuning, the positioning error is reduced by at least 39.31%. The positioning technology developed in this study can achieve good hydrogen leak source location accuracy in a fully confined space only with the help of HCSs and a small amount of data.