JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
Fault Diagnosis Management Model using Machine Learning |
| 영문제목(English Title) |
Fault Diagnosis Management Model using Machine Learning |
| 저자(Author) |
Xitong Yang
Jaeseung Lee
Heokyung Jung
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0128 ~ 0134 (2019. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.
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| 키워드(Keyword) |
Data analysis
Fault diagnosis
Machine learning
SVM
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| 파일첨부 |
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