Application Research Of Multivariable Statistical Process Control In Industrial Production

This dissertation investigates the application of MSPC, tries to explain the means of principle component, making MSPC more practical. The main research work includes as following:1. We tell the relation between quality management and SPC, summarize the application and development of SPC. Principal Component Analysis (PCA) technique are introduced, the and the essential of PCA is analyzed. The meaning and means to interpret the practice means of PC are brought forward. Basing on theory above, PCR (Principal Component Recursion) and PLS (Partial Least Squares) are Presented.2. Single-variable SPC and its disadvantages are pointed out, then some statistical variables and techniques of MSPC are emphasized. Multi-dimension data processing is transformed to the analysis of a single statistical variable by defining different statistical variables. Despite the standardized control chart, an IPC control scheme composed of SPC and EPC is proposed based on Minimum-Total-Cost. Respectively, on a continuous stirred tank reactor and the batch polymerization process monitor-monitoring Methyl Methacrylate Polymerization for the application background, introduces the multivariate statistical process control in a continuous process and batch process application.3. There is serious non-linear among process variables in many processing. Though the question can be solved using PLS by eliminating correlation in variables, it can not used to solve process with strong non-linearity effectively because it using linear relation to connect input and output factors. Based on the correctitude character of Chebyshev polynomial, this paper brings forward an improved Non-linear PLS and it can be used to represent broader non-linear model.