Title: Monitoring correlated variable and attribute quality characteristics based on NORTA inverse technique
Authors: Mohammad Hadi Doroudyan; Amirhossein Amiri
Addresses: Industrial Engineering Department, Faculty of Engineering, Shahed University, P.O. Box 18151/159, Tehran, Iran ' Industrial Engineering Department, Faculty of Engineering, Shahed University, P.O. Box 18151/159, Tehran, Iran
Abstract: In some statistical process control applications, quality of a process or a product is characterised and monitored based on a variable or an attribute quality characteristic. However, sometimes a vector of variables or attributes describes the quality of a process. Likewise, in some cases, quality of a process or a product is characterised by a combination of several correlated variables and attributes. To the best of our knowledge, there is no method in monitoring multivariate-attribute processes in spite of numerous studies in multivariate and multi-attribute control charts. This paper describes a method to monitor a process with multiple correlated variable and attribute quality characteristics. In the proposed method, we utilise NORTA inverse technique to design a scheme in monitoring multivariate-attribute processes. First, NORTA inverse method transforms the data to a multivariate normal distribution, and then we apply multivariate control charts such as T² and MEWMA for transformed data. The performance of the proposed method considering both T² and MEWMA charts is investigated by using simulation studies in terms of average run length criterion.
Keywords: statistical process control; SPC; correlated multivariate-attribute processes; NORTA inverse method; phase II; average run length; ARL; simulation; process quality; product quality.
International Journal of Productivity and Quality Management, 2014 Vol.14 No.2, pp.247 - 262
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 26 Aug 2014 *