Fault Diagnosis Management Model using Machine Learning

• Published in: Journal of Information and Communication Convergence Engineering, 17(2) Vol. 17; no. 2; pp. 128 - 134
• Main Authors: Yang, Xitong, Lee, Jaeseung, Jung, Heokyung
• Format: Journal Article
• Language: English
• Published: 한국정보통신학회JICCE 2019; 한국정보통신학회
• Subjects: 전자/정보통신공학; Fault diagnosis; Data analysis; SVM; Machine learning
• ISSN: 2234-8255; 2234-8883
• DOI: 10.6109/jicce.2019.17.2.128

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.