Abstract
This paper presents a method for solving the problem of recognising the leakage state of a measured object by means of a gas leak detection system, using an ultrasonic method based on data fusion and neural networks. The neural network is trained using cross-correlation information from a probe as the prior probability, combined with the Dempster-Shafer (D-S) evidential reasoning method, and then applied in the gas leak ultrasonic detection system. Experimental results show that recognition based on this combination is significantly better than with a single sensor. Consequently, the validity and correctness of this method have been verified.
| Original language | English |
|---|---|
| Pages (from-to) | 189-194 |
| Number of pages | 6 |
| Journal | Insight: Non-Destructive Testing and Condition Monitoring |
| Volume | 56 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Apr 2014 |
Keywords
- Data fusion
- Evidential theory
- Gas leak detection
- Neural networks
- Ultrasonics
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Tao, W., Yu, P., Huiheng, X., & Wei, F. (2014). Detection of small gas leaks based on neural networks and D-S evidential theory using ultrasonics. Insight: Non-Destructive Testing and Condition Monitoring, 56(4), 189-194. https://doi.org/10.1784/insi.2014.56.4.189