Name
Abstract | ||
|---|---|---|
Usually, it is subjective, laborious, and inefficient about the traditional water-based air-tightness test. In this article, a mechanism and method of leak detection for a pressure vessel is proposed. First, through collecting and analyzing sequential pressure data, the beginning time of the air-tightness test phase is determined. Second, pressure vessels are located using a deep convolutional neural network (DCNN)-based method and relocation strategy. Third, a method that employs a subregion voting strategy, background modeling, and sequential pressure data is proposed to determine whether, where, and how a pressure vessel leaks. An improved pressure vessel air-tightness test mechanism is developed to provide the basic support for leak detection framework and improve test efficiency. On the independent data set, the accuracy of the proposed leak location method is 98%, and the maximum error of the leakage calculation is 2.14 mL/min. Experiments show that the proposed leak detection framework and mechanism can greatly improve the efficiency and reliability of pressure vessel leak detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2020.2969300 | IEEE Transactions on Instrumentation and Measurement |
Keywords | DocType | Volume |
Air-tightness detection,bubble detection,deep convolutional neural networks (DCNNs),machine vision,pressure vessel | Journal | 69 |
Issue | ISSN | Citations |
9 | 0018-9456 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Gao | 1 | 18 | 9.17 |
| Junhui Lin | 2 | 0 | 0.34 |
| Yisu Ge | 3 | 0 | 2.03 |
| Shufang Lu | 4 | 23 | 7.75 |
| Yuanming Zhang | 5 | 5 | 4.48 |