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http://dx.doi.org/10.5762/KAIS.2018.19.3.40

On-line Process Data-driven Diagnostics Using Statistical Techniques  

Cho, Hyun-Woo (Department of Industrial and Management Engineering, Daegu University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.19, no.3, 2018 , pp. 40-45 More about this Journal
Abstract
Intelligent monitoring and diagnosis of production processes based on multivariate statistical methods has been one of important tasks for safety and quality issues. This is due to the fact that faults and unexpected events may have serious impacts on the operation of processes. This study proposes a diagnostic scheme based on effective representation of process measurement data and is evaluated using simulation process data. The effects of utilizing a preprocessing step and nonlinear statistical methods are also tested using fifteen faults of the simulation process. Results show that the proposed scheme produced more reliable results and outperformed other tested schemes with none of the filtering step and nonlinear methods. The proposed scheme is expected to be robust to process noises and easy to develop due to the lack of required rigorous mathematical process models or expert knowledge.
Keywords
Diagnosis; fault; filtering; multivariate statistical methods; process data;
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