# Statistical Process Control with Intelligence Based on the Deep Learning Model
For example, skewed patterns are caused by poor processing habits of workers, and flat-top patterns indicate that production processes are affected by slow-changing factors. As far as we know, only one paper has studied histogram pattern recognition (HPR) in SCI and EI database in recent years [4]. In [4], an HPR method based on a fuzzy ART neural network is proposed. The advantage of this method is that it can cluster HPs adaptively and generate new classes for unknown patterns. At the same time, there is a big risk to classify the known patterns into new categories, which is not conducive to practical application. Moreover, the simulation data used in this paper are not random enough. The quality data of real processes cannot be fully simulated. The lack of research on HPR is a major drawback of SPC intelligent research. This is because histograms and control charts complement each other, as shown in Figure 1. According to the theory of a control chart, the quality data is normal. However, its histogram pattern is an island pattern, which means that the production process is abnormal. Control charts are suitable for identifying short-term anomalies, while histograms can reflect the long-term distribution of quality data. Therefore, the role of a histogram cannot be ignored, and enterprises are also in urgent need of accurate automatic HPR. It is well known that in the field of pattern recognition, the input form of data has a great influence on classification, which is called the input representation problem. One form is to take the raw data as input [24; 32; 33], such as quality data in the control chart and the frequency of each interval in the histogram. Another form is to take the features extracted from the raw data as input, such as wavelet features [11], shape features [19; 34; 35] and statistical features [23; 34]. The latter is called feature engineering, that is, experts design favorable features for pattern recognition problems based on experience. Most scholars' research shows that if the dimension of input data is too large, the classifier size of traditional machine learning algorithm will be too large, which is unfavorable. Its accuracy and efficiency are often lower than the feature-based method, because the dimension of feature set is usually very small [11; 29]. There is no doubt that the key to improve classification accuracy is to select the most advantageous feature set. To achieve this goal, a genetic algorithm (GA) [1] and local linear embedding (LLE) [36] are used to optimize features from high-dimensional feature sets. However, they only select good features from the known expert feature set, and do not improve the quality of feature set from the root. The discarded raw data still has potential values, so more advanced methods, such as feature learning, still need to be used. Feature learning refers to adaptively extract the most advantageous features from the raw data by using the deep neural network (DNN), without relying on any expert experience. Since the feature set is obtained by learning and minimizing the loss function, it can be considered that this feature set is the best choice for this classification task [37]. The most representative deep learning algorithms include the deep belief network (DBN) [38; 39] based on the restricted Boltzmann machine (RBM), the convolutional neural network (CNN) [40; 41; 42] based on the convolution layer and pooling layer and the recurrent neural network (RNN) [43; 44] based on the recursive layer. They have made remarkable achievements in machine vision, natural language processing and fault diagnosis [45]. On the basis of relevant research in recent years [18; 46; 47], this paper proposes to use multilayer bidirectional long short-term memory networks (Bi-LSTM) to learn the features of histogram and control chart, and finally realize HPR and CCPR. The Bi-LSTM is an improved method of RNN. Its special gate structure enables it to capture both short-term dependencies and long-term dependencies. This paper is an extension of previous studies [18].