# Statistical Process Control with Intelligence Based on the Deep Learning Model  
[PERSON]  
1 Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (T.Z. ); [EMAIL_ADDRESS] (Z.S. ); [EMAIL_ADDRESS] (M.W. ); [EMAIL_ADDRESS] (X.G. ); [EMAIL_ADDRESS] (D.C.) 1  
[PERSON]  
1 Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (T.Z. ); [EMAIL_ADDRESS] (Z.S. ); [EMAIL_ADDRESS] (M.W. ); gaoxsh@bjut. In the early application of control charts, it is necessary to manually determine whether or not there is any donor-mality in the control charts and what kind of abnormality occurs. It is easy to detect abnormalities beyond the control limit, but difficult to identify abnormal patterns within the control limit, which is easily affected by the experience level of quality control personnel [6]. Then, many non-natural pattern detection methods based on supplementary rules were proposed by scholars [7; 8; 9]. The Nelson criterion is one of these methods [10]. However, control charts contain a lot of information about the production process. Supplementary rules cannot describe the specific modes of a process in detail. In addition, a large number of rules are used, which is not conducive to the real-time monitoring of production process, and will cause many false alarms [11; 12]. In order to realize automated control chart pattern recognition (CCPR), scholars have designed a series of expert systems so that quality management personnel can take timely remedial measures for uncontrolled manufacturing process [13; 14; 15; 16]. They are mainly based on statistical tests and heuristic algorithms [17]. Because of the shortcomings of these judgment rules, early expert systems cannot get satisfactory results [18]. This stimulates interest in developing more accurate CCPR algorithms. Because machine learning algorithms have strong pattern recognition capabilities, their applications in the CCPR field have received more and more attention and achieved some success. It mainly includes an artificial neural network (ANN) and a support vector machine (SVM). The multilayer perceptron (MLP) are the most widely used in ANN-based methods [19; 20; 21]. In [22], an effective CCPR system based on MLP is studied. The effects of different layers of MLP and different training algorithms on the results are also compared. It is found that the resilient back-propagation (RBP) algorithm has the best convergence speed and the Levenberg-Marquardt (LM) algorithm has the best optimization effect. In [23], a new learning algorithm based on bees algorithm is adopted, and an optimized radial basis function neural network (RBFNN) is trained, which shows good performance in CCPR tasks. In addition, the probability neural network (PNN) [24], spiking neural network (SNN) [25] and learning vector quantization (LVQ) [26; 27; 28] are also widely used. They all belong to supervised learning algorithms. At the same time, unsupervised algorithms such as self-organizing mapping (SOM) [29] and adaptive resonance theory (ART) are also used in CCPR. Because of its shallow structure and limited learning ability, ANN has some shortcomings, such as difficulty in convergence and can easily fall into local minimum, which limits its further development. Subsequently, SVM and its variants have been proposed by scholars and used to solve CCPR problems. For example, weighted SVM [30], multi-kernel SVM [31] and fuzzy SVM [1]. They generally show better recognition accuracy than traditional ANN. Histogram is a graphical representation of the distribution of quality data. Its distribution degree is a common tool for judging and predicting the quality of production process. Similar to the control chart pattern (CCP), the abnormality of the histogram pattern (HP) corresponds to specific factors.