Abstract
Control chart patterns (CCPs) are often used for quality control in the manufacturing process, and effective recognition of these patterns is critical to manufacturing. In the dynamic production process, the raw data and features of CCPs are used to recognize or further predict the trends. However, the inaccuracy of CCPs information extraction, loss of information, and complex recognizer can lead to the difficulty of recognition. In order to improve the accuracy of information extraction and recognition, a CCPs recognition method based on optimized deep belief network (DBN) and data information enhancement was proposed. Adaptive features selection and information enhancement (AFIE) was used to select the most appropriate features and make these features combine with the raw data to from the dataset in order to reduce the data dimension, and then combine dimensioned data with the selected features to enhance the data information. Further, this study presented a DBN with three restricted Boltzmann machine structures, which was optimized by using the artificial fish swarm algorithm (AFSA). The method of AFIE was discussed to obtain the optimal data set, and parameters of the network structure were analyzed, optimized, and discussed based on experiments and AFSA. At the same time, this method was compared with multi-layer perceptron neural network. The simulation results showed that the method proposed in this study exhibited excellent effect, and the recognition accuracy achieved by this method was 99.78% for 2000 samples of each pattern.
Highlights
With the development of intelligent manufacturing, control chart patterns (CCPs) have been widely used as an important means of quality control
In complex production environments, CCPs are widely used for recognition or prediction to realize intelligent production and improve production quality; when pattern recognition is carried out, the data information is weak and the instability causes the recognition error
According to the research results, the main conclusions are as follows: (1) AFIE method obtains effective features and the method applies kernel principal component analysis (KPCA) to reduce the dimension of raw data and selected features
Summary
With the development of intelligent manufacturing, control chart patterns (CCPs) have been widely used as an important means of quality control. H.Y. Chu et al: Control chart pattern recognition based on optimized deep belief neural network and data information enhancement [10] [11]. In the dynamic production process, due to the raw data or extraction of inappropriate feature, it causes the loss of part of the data recognition information. Xanthopoulos et al [28] proposed a method for CCPs recognition based on SVM with weighted support vector, which exhibited good performance. Combination of adaptive features selection and information enhancement (AFIE) method, intelligent optimization algorithm, and DNN method was proposed to improve the recognition performance of CCPs. The AFIE method was used to extract the features of the raw data and select the features with great impact.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
