Principal Component Analysis and Automatic Relevance Determination in Damage Identification

This paper compares two neural network input selection schemes, the Principal Component Analysis (PCA) and the Automatic Relevance Determination (ARD) based on MacKay's evidence framework. The PCA takes all the input data and projects it onto a lower dimension space, thereby reducing the dimension of the input space. This input reduction method often results with parameters that have significant influence on the dynamics of the data being diluted by those that do not influence the dynamics of the data. The ARD selects the most relevant input parameters and discards those that do not contribute significantly to the dynamics of the data being modelled. The ARD sometimes results with important input parameters being discarded thereby compromising the dynamics of the data. The PCA and ARD methods are implemented together with a MultiLayer-Perceptron (MLP) network for fault identification in structures and the performance of the two methods is assessed. It is observed that ARD and PCA give similar accuracy levels when used as input-selection schemes. Therefore, the choice of input-selection scheme is dependent on the nature of the data being processed.