Quality Control Method Based on Quantum Genetic Clustering Algorithm

doi:10.16182/j.issn1004731x.joss.19-FZ0256

Abstract

Abstract: This paper proposes a method of quality control chart recognition based on Quantum Genetic Clustering Algorithm. This method is divided into two parts: quality feature extraction and pattern classification. By combining Quantum Genetic Algorithm(QGA) and K-means algorithm, a quantum genetic clustering algorithm based on a mechanism for determining the rotation direction of a quantum rotary gate is proposed, and its performance is verified by experimental simulation. Based on the clustering analysis of quality data using the quantum genetic algorithm proposed in this paper, a control chart feature description method is proposed. With this feature as input, Support Vector Machine is used to identify the corresponding quality control chart pattern.The proposed quantum genetic clustering algorithm obtains better clustering results,and the accuracy of the proposed control chart recognition method reaches 98.63%.

Key words: quality control, GA, control chart, K-means, QGA

CLC Number:

TP391.9

Wang Jie, Wang Yan. Quality Control Method Based on Quantum Genetic Clustering Algorithm[J]. Journal of System Simulation, 2019, 31(12): 2591-2599.

References

[1] Addeh J, Ebrahimzadeh A, Ranaee V.Control chart pattern recognition using adaptive back-propagation artificial Neural networks and efficient features[C]. International Conference on Control. Bandung,Indonesia: IEEE, 2011.
[2] Xanthopoulos P, Razzaghi T.A Weighted Support Vector Machine Method for Control Chart Pattern Recognition[J]. Computers & Industrial Engineering (S0360-8352), 2014, 70(1): 134-149.
[3] Zhang Y, Lin X.Recognition method for control chart patterns based on improved sequential forward selection and extreme learning machine[C]. International Conference on Progress in Informatics & Computing. Shanghai,China: IEEE, 2014.
[4] Ranaee V, Ebrahimzadeh A, Ghaderi R.Application of the PSO-SVM model for recognition of control chart patterns[J]. Isa Transactions (S0019-0578), 2010, 49(4): 577-586.
[5] Lu Y, Mathur A K, Blunt B A, et al.Dual X-ray absorptiometry quality control: Comparison of visual examination and process-control charts[J]. Journal of Bone & Mineral Research (S0884-0431), 2010, 11(5): 626-637.
[6] 孙静. 最新国家标准GB/T4091-2001《常规控制图》理解与实施[M]. 北京: 中国标准出版社, 2002.
Sun Jing.The latest national standard GB/T 4091-2001“General Control Chart” understands and implements[M]. Beijing: China Standard Publishing House, 2002.
[7] 刘玉敏, 周昊飞. 基于多特征混合与支持向量机的动态过程异常监控[J]. 计算机集成制造系统, 2015, 21(10): 2637-2643.
Liu Yumin, Zhou Haofei.Dynamic process anomaly monitoring based on multi-feature mixing and support vector machines[J]. Computer Integrated Manufacturing Systems, 2015, 21(10): 2637-2643.
[8] Milo M W, Roan M, Harris B.A new statistical approach to automated quality control in manufacturing processes[J]. Journal of Manufacturing Systems (S0278-6125), 2015, 36: 159-167.
[9] 戴晓晖, 李敏强, 寇纪淞. 遗传算法理论研究综述[J]. 控制与决策, 2000, 15(3): 263-268.
Dai Xiaohui, Li Minqiang, Kou Jisong.Overview of theoretical research on genetic algorithms[J]. Control and Decision-Making, 2000, 15(3): 263-268.
[10] Qiu D, Tamhane A C.A comparative study of the K-means algorithm and the normal mixture model for clustering. Univariate case[J]. Journal of Statistical Planning & Inference (S0378-3758), 2007, 137(11): 3722-3740.
[11] 陆林花, 王波. 一种改进的遗传聚类算法[J]. 计算机工程与应用, 2007, 43(21): 170-172.
Lu Linhua, Wang Bo.An improved genetic clustering algorithm[J]. Computer Engineering and Applications, 2007, 43(21): 170-172.
[12] 刘靖明, 韩丽川, 侯立文. 基于粒子群的K均值聚类算法[J]. 系统工程理论与实践, 2005, 25(6): 54-58.
Liu Jingming, Han Lichuan, Hou Liwen.K-means clustering algorithm based on particle groups[J]. Systems Engineering Theory and Practice, 2005, 25(6): 54-58.
[13] Addeh A, Khormali A, Golilarz N A.Control chart pattern recognition using RBF neural network with new training algorithm and practical features[J]. ISA Transactions (S0019-0578), 2018, 79(8): 202-216.
[14] 张敏, 程文明. 基于自适应粒子群算法和支持向量机的控制图模式识别[J]. 工业工程, 2012, 15(5): 125-129.
Zhang Min, Cheng Wenming.Control graph pattern recognition based on adaptive particle swarm algorithm and support vector machine[J]. Industrial Engineering, 2012, 15(5): 125-129.
[15] Wu C, Liu F, Wang M.Recognition of Control Chart Patterns Using a Support Vector Machine based Classifier with Features extracted from clustering[J]. Metalurgia International (S1582-2214), 2013, 18(6): 5-10.
[16] Chinas P, Lopez I, Vazquez J, et al.SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data[J]. IEEE Latin America Transactions (S1548-0992), 2015, 13(5): 1633-1639.