Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 1 1 2012 02 20 Development of a Time-Based Segmentation Method for Phase I Profile Monitoring Development of a Time-Based Segmentation Method for Phase I Profile Monitoring 28 38 234962 FA Abbas Saghai Department of Industrial Engineering, Faculty of Engineering, Islamic .Azad University, Science and Research Branch, Tehran, Iran Elahe Gholamzadeh Nabati Department of Industrial Engineering, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran Journal Article Sometimes, a quality characteristic is expressed as a specific functional relationship, which is referred to as a profile in statistical quality control. In this paper, linear profile analysis in Phase I control charts is investigated from a new perspective. In statistical quality control, it is usually assumed that the process has a constant mean. However, there may be processes where the mean of the parameters is not constant over time, and this variability is an inherent characteristic of the process. For controlling such processes, most existing control chart methods are ineffective. To statistically monitor such processes in Phase I, it is necessary to segment the process into sections where it is stable and consistent before performing statistical process control. For process segmentation, few studies have proposed the use of time-based clustering techniques, and the existing studies have mainly focused on univariate data. In this paper, we develop a process segmentation method for profile monitoring. Different segmentation algorithms for functional data are compared, and the results are reported. Studies show that the functional data clustering algorithm based on the Fuzzy c-means method performs well for profile segmentation. Sometimes, a quality characteristic is expressed as a specific functional relationship, which is referred to as a profile in statistical quality control. In this paper, linear profile analysis in Phase I control charts is investigated from a new perspective. In statistical quality control, it is usually assumed that the process has a constant mean. However, there may be processes where the mean of the parameters is not constant over time, and this variability is an inherent characteristic of the process. For controlling such processes, most existing control chart methods are ineffective. To statistically monitor such processes in Phase I, it is necessary to segment the process into sections where it is stable and consistent before performing statistical process control. For process segmentation, few studies have proposed the use of time-based clustering techniques, and the existing studies have mainly focused on univariate data. In this paper, we develop a process segmentation method for profile monitoring. Different segmentation algorithms for functional data are compared, and the results are reported. Studies show that the functional data clustering algorithm based on the Fuzzy c-means method performs well for profile segmentation.