An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products

Image-capturing systems are increasingly being used in manufacturing shop floors since they can reliably capture important aesthetic information pertaining to the quality of manufactured parts in real time. State-of-the-art image-monitoring applications have focused on the detection of a single fault; however, the number of fault clusters per image in industrial applications can be numerous. To address this issue, we propose the use of a multivariate generalized likelihood ratio (MGLR) control chart for monitoring industrial products whose quality is described by a specific pattern (e.g. uniform patterns in LED screens or decorative patterns in textile products). Our method is specifically designed for greyscale images that are typical outputs of real-time industrial image-capturing systems. Extensive computer simulations show that the proposed method can detect the occurrence of single and multiple faults. We also present an experimental study to highlight how practitioners can implement and make use of the MGLR control chart in image-monitoring applications.