On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines

• Published in: International journal of production research Vol. 50; no. 22; pp. 6288 - 6310
• Main Authors: Du, Shichang, Lv, Jun, Xi, Lifeng
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 15.11.2012; Taylor & Francis; Taylor & Francis LLC
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Classification; Computer science; control theory; systems; Computer simulation; Control charts; Data processing. List processing. Character string processing; ensemble classification; Exact sciences and technology; Guidelines; Inventory control, production control. Distribution; Memory organisation. Data processing; Monitors; multivariate statistical process control; On-line systems; Operational research and scientific management; Operational research. Management science; Operations research; Process controls; process mean shifts; Simulation; Software; Statistical process control; Statistics; Studies; support vector machine; Support vector machines; Theoretical computing; Statistical process control; Multivariate process; Control chart; Statistical analysis; On line; Computer simulation; Evolutionary algorithm; Multivariate analysis; Particle swarm optimization; multivariate statistical process control; Aggregate model; support vector machine; ensemble classification; Efficiency; Manufacturing process; process mean shifts; Quality control; Simulated annealing; Vector support machine; Anomaly; Swarm intelligence; Causality; Multiple classification; Production process
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2011.631596

In multivariate statistical process control (MSPC), most multivariate control charts can effectively monitor anomalies based on overall statistic, however, they cannot provide guidelines to classify the source(s) of out-of-control signals. Classifying the source(s) of process mean shifts is critical for quality control in multivariate manufacturing process since the immediate identification of them can greatly help quality engineer to narrow down the set of possible root causes and take corrective actions. This study presents an improved particle swarm optimisation with simulated annealing-based selective multiclass support vector machines ensemble (PS-SVME) approach, in which some selective multiclass SVMs are jointly used for classifying the source(s) of process mean shifts in multivariate control charts. The performance of the proposed PS-SVME approach is evaluated by computing its classification accuracy. Simulation experiments are conducted and a real application is illustrated to validate the effectiveness of the developed approach. The analysis results indicate that the developed PS-SVME approach can perform effectively for classifying the source(s) of process mean shifts.