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http://dx.doi.org/10.9709/JKSS.2011.20.4.067

Model Parameter Based Fault Detection for Time-series Data  

Park, Si-Jeo (고려대학교 산업경영공학과)
Park, Cheong-Sool (고려대학교 산업경영공학과)
Kim, Sung-Shick (고려대학교 산업경영공학과)
Baek, Jun-Geol (고려대학교 산업경영공학과)
Publication Information
Journal of the Korea Society for Simulation / v.20, no.4, 2011 , pp. 67-79 More about this Journal
Abstract
The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.
Keywords
Time-series; Fault detection; Parameter monitoring; $K^2$-control chart; model-based; MPBC;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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