Recognition of abnormal patterns in industrial processes with variable window size via convolutional neural networks and AdaBoost

In industrial settings, it is inevitable to encounter abnormal patterns monitoring a process. These patterns point out manufacturing faults that can lead to significant internal and external failure costs unless treated promptly. Thus, detecting such abnormalities is of utmost importance. Machine learning algorithms have been widely applied to this problem. Nevertheless, the existing control chart pattern recognition (CCPR) method can only deal with a fixed input size rather than dealing with different input sizes according to the actual production needs. In order to tackle this problem, an original CCPR method relying on convolutional neural network (CNN) named as VIS-CNN is proposed. Signal resizing is performed using resampling methods, then CNN is used to extract the abnormal patterns in the dataset. Five different input sizes are generated for model training and testing. The optimal hyperparameters, as well as the best structure of the used CNN are obtained using Bayesian Optimization. Simulation results show that the correct recognition rate of the VIS-CNN is 99.78%, based on different window size control charts. Furthermore, we address the issue of the mixed CCP and provide a modified scheme to achieve high recognition ratio for 8 mixed patterns on top of 6 standard patterns. The modified scheme includes wavelet noise reduction and Adaptive Boosting. A case study on metal galvanization process is presented to show that the method has potential applications in the industrial environment.