Detection of small gas leaks based on neural networks and D-S evidential theory using ultrasonics
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.
Keywords: DATA FUSION; EVIDENTIAL THEORY; GAS LEAK DETECTION; NEURAL NETWORKS; ULTRASONICS
Document Type: Research Article
Publication date: 01 April 2014
- Official Journal of The British Institute of Non-Destructive Testing - includes original research and development papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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