Phase II monitoring of multivariate simple linear profiles |
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| Authors: | R Noorossana M Eyvazian A Vaghefi |
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| Affiliation: | 1. Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran;2. LUNAM Université, Université de Nantes & IRCCyN UMR CNRS 6597, Nantes, France;1. Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar;2. Department of Industrial Engineering, University of Eyvanekey, Eyvanekey, Iran;3. Department of Business Analytics and Actuarial Science, Siena College, NY, USA;4. College of North Atlantic, Doha, Qatar;1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong;2. School of Management, Universiti Sains Malaysia, 11800 Penang, Malaysia;3. School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Penang, Malaysia;4. Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada;1. Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan;2. School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia;1. Jilin Medical University, Jilin 132013, PR China;2. Institute of Statistics and LPMC, Nankai University, Tianjin 300071, PR China;3. School of Science, Tianjin University of Technology and Education, Tianjin 300222, PR China |
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| Abstract: | In some quality control applications, quality of a product or process can be characterized by a relationship between two or more variables that is typically referred to as profile. Moreover, in some situations, there are several correlated quality characteristics, which can be modeled as a set of linear functions of one explanatory variable. We refer to this as multivariate simple linear profiles structure. In this paper, we propose the use of three control chart schemes for Phase II monitoring of multivariate simple linear profiles. The statistical performance of the proposed methods is evaluated in term of average run length criterion and reveals that the control chart schemes are effective in detecting shifts in the process parameters. In addition, the applicability of the proposed methods is illustrated using a real case of calibration application. |
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