A Deep-Learning-Based Low-Cost Micro-Leakage Measurement System for Industrial Applications

Abstract

Serious leakage will cause environmental pollution and safety problems, so strict leakage measurement is necessary for sealed products before leaving the factory. In this article, a semantic-segmentation-based micro-leakage measurement framework (MLMF) is proposed, and the core modules in MLMF are designed and analyzed. First, the DeepLabv3+ algorithm is improved from three aspects, including the design of the fusion layer, the introduction of the attention mechanism of the segmentation model, and the reconstruction of the loss function, which improve the precision of image segmentation tasks. Then, the volume of the intermediate cavity (ICV), which affects the accuracy of leakage measurement, is studied, and the reasonable selection interval of the ICV has been obtained. Finally, through the experimental analysis of the influencing factors of the bubble characteristics in the two-phase flow, the key parameters affecting the bubble shape are found, and the control of the initial shape, generation period, and oscillation process of the bubble is realized. The experimental analysis is carried out through the established experimental platform to verify the effectiveness of the proposed MLMF. The bubble method is innovatively combined with deep learning, and a series of problems, such as bubble shape control and liquid media selection, are solved. It provides a novel low-cost dry measurement mechanism for the field of micro-leakage measurement of sealed vessels.