Genetic Algorithms Applied to PCA–Residues Optimization for Defect Localization

While defect localization is vital in real-world systems, some limitations inherent to the existing techniques urge us to seek more advanced methods. This paper presents a new approach which takes advantages of genetic algorithms for optimization of non-convex objective function employed in calculating structured residues. The proposed approach so far improved the current principal component analysis (PCA) based on one of the defect localization. It has excellent impact on problem solving while dealing with optimization of residues structuring. The first part illustrates both the PCA model and the traditional residues structuring approach. The principle of optimizing a problem via genetic algorithms is explained later. A proposed objective function to be optimized is defined in the next part, and its optimization via genetic algorithms allows the structured residues computation. The new approach has been applied and proved functional for monitoring the Tennessee Eastman process. We have also proved the efficient performance of the proposed method in comparing it with some state-of-the-art methods.