Integrated Deep Learning and Statistical Process Control for Online Monitoring of Manufacturing Processes

Advancements in online sensing technologies and wireless networking has reshaped the competitive landscape of manufacturing systems, leading to exponential growth of data. Among various data types, high-dimensional data sources such as images and videos play an important role in process monitoring. Efficient utilization of such sources can help systems reach high accuracy in fault diagnosis. On the other hand, while the researches on statistical process control (SPC) tools are tremendous, the application of SPC tools considering high-dimensional data sets has received less attention due to their complexity. In this paper, we try to address this gap by designing and developing a hybrid model based on deep learning (DL) and SPC models to monitor the manufacturing process in presence of high-dimensional data. In particular, we first apply a Fast Region-based Convolutional Network method referred to Fast R-CNN in order to monitor the image sequences over time. Then, some statistical features are derived and plotted on the multivariate exponentially weighted moving average (EWMA) control chart. The effectiveness of proposed hybrid model is illustrated through a numerical example.