Data Mining-driven Approahces For Process Monitoring And Diagnosis

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Title: Data Mining-driven Approahces For Process Monitoring And Diagnosis
Author: Sukchotrat, Thuntee
Abstract: The objective of this dissertation is to develop a new set of efficient process monitoring and diagnostic tools through their integration with data mining algorithms . Statistical process control (SPC ) is one of the most widely used techniques for quality control . Although traditional SPC tools are effective in simple manufacturing processes that generate a small volume of independent data , these tools falter when confronted by the large streams of complex and correlated data found in modern manufacturing systems . As the limitations of SPC methodology become increasingly obvious in the face of ever more complex manufacturing processes , data mining , because of its proven capabilities to analyze and manage large amounts of data , has the potential to resolve the problems that are stretching SPC to its limits . This dissertation consists of three components .First , we propose a new class of control charts that take advantage of available out -of -control information to improve the detection efficiency . The proposed charts integrate a traditional multivariate control chart technique with a supervised classification algorithm . We call the proposed chart the “ Probability of Class (PoC ) chart ” because the values of the PoC , obtained from classification algorithms , are used as monitoring statistics . The control limits of PoC charts are established and adjusted by the misclassification cost . Second , we propose a collection of new control charts , based on one -class classification algorithms to improve both phase I and phase II analyses in SPC . The proposed one -class classification -based control charts plots a monitoring statistic that represents the degree of being an outlier obtained through the one -class classification algorithm . The control limits of the proposed charts are established based on the empirical level of significance on the quantile estimated by the bootstrap method . Third , we propose a nonparametric false isolation approach in multivariate SPC through monitoring statistics obtained from the one -class classification -based control charts .The monitoring statistics obtained from one -class classification are decomposed into individual components that reflect the contribution of individual variables to the fault signal . The threshold derived from the bootstrap -quantile estimated method can help indicate the significance of these variables . The novelty of this dissertation is the integration of perspectives from data mining , quality engineering , and statistics that recognizes their shared goals while highlighting their key differences , so as to enable new methodologies for overcoming longstanding research problems and challenges appearing in modern manufacturing /service systems .
URI: http : / /hdl .handle .net /10106 /1827
Date: 2009-09-16

Citation

Data Mining-driven Approahces For Process Monitoring And Diagnosis. Available electronically from http : / /hdl .handle .net /10106 /1827 .

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