Design and Prototyping of an Industrial Fault Clustering System Combining Image Processing and Artificial Neural Network Based Approaches
Fault diagnosis of optical devices in industrial environment is a challenging but crucial task, since it ensures products’ nominal specification and manufacturing control. Defects detection and issued information processing are among chief phases for succeeding in such diagnosis. A new scratches and digs defects detection and characterization method exploiting Nomarski microscopy issued imaging has been developed. It allows automatic check of optical devices during industrial process. Issued images contain several items which have to be detected and then classified in order to discriminate between “false” defects and “abiding” ones. In this paper, a processing method is proposed for a first step of pattern recognition from Nomarski images. A first phase permits to extract items images and a second phase allows us to cluster them using an unsupervised neural network technique, Self-Organizing Map.
KeywordsFault Diagnosis Defect Image Correctable Defect Nominal Specification Automatic Check
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