Recognition of mixture control chart patterns based on fusion feature reduction and fireworks algorithm-optimized MSVM

• Published in: Pattern analysis and applications : PAA Vol. 23; no. 1; pp. 15 - 26
• Main Authors: Zhang, Min, Yuan, Yi, Wang, Ruiqi, Cheng, Wenming
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2020; Springer Nature B.V
• Subjects: Algorithms; Classifiers; Computer Science; Computer simulation; Control charts; Data processing; Fireworks; Genetic algorithms; Kernel functions; Machine learning; Parameters; Particle swarm optimization; Pattern Recognition; Principal components analysis; Reduction; Support vector machines; Theoretical Advances; Fireworks algorithm; Parameters optimization; Fusion feature reduction; Multiclass support vector machines; Control chart patterns recognition
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-018-0748-6

Unnatural control chart patterns (CCPs) can be associated with the quality problems of the production process. It is quite critical to detect and identify these patterns effectively based on process data. Various machine learning techniques to CCPs recognition have been studied on the process only suffer from basic CCPs of unnatural patterns. Practical production process data may be the combination of two or more basic patterns simultaneously in reality. This paper proposes a mixture CCPs recognition method based on fusion feature reduction (FFR) and fireworks algorithm-optimized multiclass support vector machine (MSVM). FFR algorithm consists of three main sub-networks: statistical and shape features, features fusion and kernel principal component analysis feature dimensionality reduction, which make the features more effective. In MSVM classifier algorithm, the kernel function parameters play a very significant role in mixture CCPs recognition accuracy. Therefore, fireworks algorithm is proposed to select the two-dimensional parameters of the classifier. The results of the proposed algorithm are benchmarked with popular genetic algorithm and particle swarm optimization methods. Simulation results demonstrate that the proposed method can gain the higher recognition accuracy and significantly reduce the running time.