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http://dx.doi.org/10.7469/JKSQM.2019.47.4.783

A Study on the Prediction Diagnosis System Improvement by Error Terms and Learning Methodologies Application  

Kim, Myung Joon (Department of Business Statistics, Hannam University)
Park, Youngho (Department of Business Statistics, Hannam University)
Kim, Tai Kyoo (Department of Business Statistics, Hannam University)
Jung, Jae-Seok (Technology and Research Center, Korea Midland Power Co.)
Publication Information
Abstract
Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.
Keywords
Support Vector Machine; Multi Layer Perception; Error Term;
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