Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residuals—the difference beten the actual value and the fitted value—with the traditional SPC approach. Hover, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful informat...
['Seoung Bum Kim', 'Weerawat Jitpitaklert', 'Thuntee Sukchotrat']
I am writing a research paper on a idea One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes
[{"from": "human", "value": "I am writing a research paper on a idea One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residuals\u2014the difference beten the actual value and the fitted value\u2014with the traditional SPC approach. Hover, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful informat..."}]