Affiliation:
1. State Key Laboratory of Mechanical Manufacturing Systems Engineering, Xi’an Jiao Tong University, Xi’an 710049, China
2. Institute of New Energy Equipment and Quality Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Abstract
Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.
Funder
National Key Research and Development Program of China
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Cited by
4 articles.
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