Journal of information and communication convergence engineering

  • 제17권2호
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  • Pages.128-134
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  • 2019
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Fault Diagnosis Management Model using Machine Learning

  • Yang, Xitong (Division of HSBC Software Development Limited Xi An Branch) ;
  • Lee, Jaeseung (Department of Computer Engineering, Pai Chai University) ;
  • Jung, Heokyung (Department of Computer Engineering, Pai Chai University)
  • 투고 : 2019.03.21
  • 심사 : 2019.05.22
  • 발행 : 2019.06.30
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초록

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.

키워드

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Fig. 1. Diagnosis model use case diagram.

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Fig. 2. Data preprocessing of SVM-based diagnosis model.

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Fig. 3. Process flowchart of the SVM-based diagnosis model.

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Fig. 4. Functions of SVM-based diagnosis model.

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Fig. 5. Data preprocessing of one-class SVM-based diagnosis model.

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Fig. 6. Process flowchart of one-class SVM-based diagnosis model.

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Fig. 7. Functions of one-class SVM-based diagnosis model.

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Fig. 8. Class diagram of SVM-based diagnosis model.

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Fig. 9. Sequence diagram of SVM-based diagnosis model.

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Fig. 10. Class diagram of one-class SVM-based diagnosis model.

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Fig. 11. Sequence diagram of one-class SVM-based diagnosis model.

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Fig. 12. ROC curve of SVM-based diagnosis model.

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Fig. 13. Accuracy of SVM-based diagnosis model.

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Fig. 14. Results of one-class SVM-based diagnosis model.

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Fig. 15. AUC of one-class SVM-based diagnostic model.

Table 1. Development environment

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Table 2. Accuracy of SVM-based diagnostic model by experiment iteration

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Table 3. Accuracy of one class SVM-based diagnostic model by experiment iteration

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참고문헌

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피인용 문헌

  1. 딥러닝 기반의 구조물 화재 재난 시 최적 대피로 안내 시스템 vol.23, pp.11, 2019, https://doi.org/10.6109/jkiice.2019.23.11.1371
  2. Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier vol.10, pp.23, 2019, https://doi.org/10.3390/electronics10232888