Detection of gas leakage sound using modular neural networks for unknown environments
It is important to detect flammable or poisonous gas leaked from the cracks in pipes of petroleum refining plants or chemical plants. We applied a novel strategy of construction of neural network to the acoustic diagnosis technique for the gas leakage. An example of the modular neural network to realize the strategy is able to adapt its structure according to the dynamic environment. Experiments were performed for an artificial gas leakage device under various experimental conditions over about 18 months in a petroleum refining plant. Experimental results showed that the proposed network could adapt the structure to changes in environments and its performance was superior to that of feed-forward networks with the re-training strategy. From these results, we confirmed the effectiveness of the modular neural network for practical use.
gas leakage sound modular neural networks acoustic diagnosis radial basis function networks