# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 4 Experiment and Discussion
### Comparison of CCPR Results of Several Methods  
In order to further verify the effectiveness of the proposed method in CCPR tasks, we compare multilayer Bi-LSTM with traditional machine learning methods (MLP) and several deep learning methods (DBN and 1D-CNN). Among them, MLP is widely used in CCPR field [19; 21; 22], which is the reason why we compare with it in this paper. The network structure of these methods is exactly the same as was used in Section 4.5. The difference is the input of the network. The input of MLP becomes CCP feature set, which includes statistical features (mean, standard deviation, skewness and kurtosis) and shape features (S, NC1, NC2, APML and APSL). These features are designed by experts in this field, and have been proved to be very effective in many years of application [18; 19; 33; 34; 35]. Therefore, the first input layer of MLP has nine neurons, which are used to receive the above nine features respectively. However, the input of the three methods based on deep learning is still the raw data, that is, the quality data on the control chart. This is because they can adaptively extract the best features from the raw data. The results are shown in Figure 10 and Table 5.  
Figure 10 shows that there are different levels of confusion in the recognition results of several CCPR methods. The most serious confusion is the DBN method. There are several of Type I errors and Type II errors (missed disturbances) in its identification results, which cannot be applied to the quality control of actual production. Although it is a deep learning method, which can extract features from the raw data through RBM pre-training, its learning ability is limited and cannot retrieve satisfactory recognition results. On the contrary, an expert feature set helps MLP get a better result, which effectively reduces the occurrence of two type errors. The validity of recognition method based on the expert feature set is proved. However, the disadvantage is that there is a little confusion between UT and US, DT and DS. More serious confusion occurs between CYC and MIX, which indicates that the existing expert feature set is not sensitive to distinguish the two CCPs. More acute features are yet to be explored. In addition, 1D-CNN got a better classification result, which reduced the confusion  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pattern** & **Parameters** & **Mathematical Representation** & **Parameter Value/Range** \\ \hline NOR & Mean \(\mu\), standard & \(y(t)=\mu+x(t)\) & \(\mu=30\), \\  & deviation \(\sigma\) & \(y(t)=\mu+x(t)+d\times t\) & \(d\in[0.15\sigma,\ 0.3\sigma]\) \\ UT & Gradient \(d\) & \(y(t)=\mu+x(t)-d\times t\) & \(d\in[0.15\sigma,\ 0.3\sigma]\) \\  
between CYC and MIX, and proves the effectiveness of processing one-dimensional data and the ability to capture the details of raw data. The recognition result of multilayer Bi-LSTM is the most satisfactory and the confusion rate is the lowest, which shows that multilayer Bi-LSTM has a strong ability of self-adaptive extraction of the best features from the raw data. The CRR reaches 99.26%, which can achieve accurate CCR. Compared with other methods, it has obvious advantages. In order to further verify the feature learning ability of multilayer Bi-LSTM to the CCPs, the features extracted from the raw data are visually compared with expert features and the features extracted by 1D-CNN, and the results are shown in Figure 11. In this paper, the t-distributed stochastic neighbor embedding (t-SNE) [52] algorithm is used to reduce the dimensions of feature sets, so that they can be drawn in two-dimensional space for data visualization.