Integration of multivariate control charts and the decision tree classifier to determine the faults of the quality characteristic(s) of a melt spinning machine used in polypropylene fiber manufacturing. Part II: The application of multivariate control charts and the decision tree classifier to determine the faults of quality characteristic(s). Issue 21 (November 2021)

• Record Type: Journal Article
• Authors: Kuo, Chung-Feng Jeffrey; Huang, Chang-Chiun; Yang, Cheng-Han; Chen, Sung-Hua
• Is Part Of: Textile research journal. Volume 91:Issue 21/22(2021)
• Page Start: 2567
• Page End: 2580
• Publication Date: 2021-11
• Subjects: Melt spinning machine -- Taguchi method -- principal component analysis -- multivariate statistical control chart -- fault diagnosis -- decision tree; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/00405175211011775
• Languages: English
• ISSNs: 0040-5175

Abstract

In this study, a multivariate statistical process control was used to analyze the abnormal samples derived from the deviation of optimum processing parameters. The experimental samples derived from the optimum processing parameters were applied as the optimal historical data to determine the control limit, and then the T 2 value was obtained from Hotelling's T 2 method. If the T 2 value exceeds the control limit, the corresponding sample is considered as abnormal. After that, the Runger, Alt and Montgomery method is used to decompose the abnormal T 2 value. Then, each quality characteristic value can be obtained and the corresponding decision tree classifier can be implemented. To improve the classification accuracy, we classify the decision tree classifier into single–double identification, single-factor abnormality and double-factor abnormality. For the individual classification test, the result showed that the accuracy of single–double identification was 98.6%, the single-factor abnormality classification was 100% and the double-factor abnormality classification was 96.0%. For the combination classification test, we can get a 98.6% accuracy rate for the single–double identification, 98.3% accuracy rate for the single-factor abnormality classification and 95.3% accuracy rate for the double-factor abnormality classification. Therefore, it can be confirmed that the proposed methods in this study can effectively identify abnormal samples and establish a fault processing parameter diagnosis system for melt spinning machines.