Fault identification in structures in the presence of missing data

This paper introduces a method for classification of faults in mechanical systems in the presence of missing input data entries. The method is based on auto-associative neural networks, where the input and the output of the network are the same. From the trained network, an error equation with missing inputs as design variables is constructed. A genetic algorithm is then used to solve for the missing input data entries. The proposed method is tested on a fault classification problem in a population of cylindrical shells. It was found that the proposed method is able to estimate missing data entries to an average accuracy of 92.5%. Furthermore, the proposed method results in a classification accuracy of 94% when used with a database that has missing input data entries, while the full database set results in a classification accuracy of 96%.