The application of artificial intelligence in health monitoring of aerospace structures
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Through life health monitoring is an important part of understanding the behaviour of aerospace structures. The prediction of aircraft structural fatigue life is vital for the safe operation of aircrafts. Real determination and monitoring of critical data such as applied load, stress/strain are key points in, extending the operational life, increasing maintenance interval. An inverse load identification problem in a simplified composite wing model is treated here by means of neural network and strain response data. It has been shown that this approach can be used to accurately predict load position and value. The network was tested to predict load across locations different from the location of the training data. © 2006 Civil-Comp Press.