Multivariate analysis of transient state infrared images in production line quality control systems

Manufacturers would like to increase production volumes while preserving the high quality of their products. The long testing times can cause a bottleneck of production processes especially taking into account the observed tendency for testing all produced devices. The main aim of this work consists in the analysis of time changes of features extracted from thermal images using the multivariate approach. The paper shows that if the principal component analysis (PCA), belonging to multivariate methods, is applied for quality control based on infrared images, then the minimum testing times can be estimated. In order to draw the final conclusions regarding testing times and, what is also very important, which principal components should be selected for classification, a detailed temporal analysis for an exemplary production line has been carried out. The future impact of the results includes higher productivity and cost-effectiveness due to the determination of an optimal decision time in production line quality control systems using the proposed approach.