The objective of this study is to evaluate a neural network monitoring system for continuous surveillance of the NASA Space Station Freedom, to detect and locate atmospheric leakage. The monitoring system uses surface mounted sensors to detect acoustic emission signals produced by the leak, which are then relayed to the neural network for source location determination. To date, acoustic emission leak location systems have achieved only limited success and are adversely affected by noise and complex geometries. For a monitoring system to be effective in locating atmospheric leakage it must have the ability to mask out noise, circumvent multipath interference, and process AE signals in real time. Neural networks seem ideally suited to the problem.
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