Loose particle classification using a new wavelet fisher discriminant method

Loose particles left inside aerospace components or equipment can cause catastrophic failure in aerospace industry. It is vital to identify the material type of these loose particles and eliminate them. This is a classification problem, and autoregressive (AR) model and Learning Vector Quantization (LVQ) networks have been used to classify loose particles inside components. More recently, the test objects have been changed from components to aerospace equipments. To improve classification accuracy, more data samples often have to be dealt with. The difficulty is that these data samples contain redundant information, and the aforementioned two conventional methods are unable to process redundant information, thus the classification accuracy is deteriorated. In this paper, the wavelet Fisher discriminant is investigated for loose particle classifications. First, the fisher model is formulated as a least squares problem with linear-in-the-parameters structure. Then, the previously proposed two-stage subset selection method is used to build a sparse wavelet Fisher model in order to reduce redundant information. Experimental results show the wavelet Fisher classification method can perform better than AR model and LVQ networks.