A New Automatic Method for Control Chart Patterns Recognition Based on ConvNet and Harris Hawks Meta Heuristic Optimization Algorithm

The productions quality has become one of the essential issues in the modern manufacturing industry and several techniques have introduced for control and monitoring the production process. Control charts are the most practical and popular tools for continuously monitoring and, if required, make adjustments to the product or process. A new automatic method based on deep learning and optimization algorithms for nine control chart patterns (CCPs) recognition are proposed in this paper. This method has two principal parts: the classification part and the tuning part. In the last few years, a convolutional neural network (ConvNet) has led to an excellent performance on various tasks, like image processing, speech recognition, and signal processing. Therefore, in the classification part, ConvNet is used as the intelligent classifier for CCPs recognition. One significant difficulty of ConvNet is that it requires considerable proficiency to select suitable parameters like a number of kernels and their spatial sizes, learning rate, etc. The ConvNet parameters have domestic dependencies which make the tuning of these parameters a challenging task. According to these issues, in the tuning part of the proposed method, the Harris hawks optimization (HHO) algorithm is used for optimal tuning of ConvNet parameters. Contrasting the common CCPs recognition methods, the proposed method takes unprocessed data and passes to more than one hidden layer for extracting the optimal feature representation instead of relying on any feature engineering mechanisms. The quantitative and simulation results show the superiority of the proposed method over the previous techniques in terms of its performance.