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Special Graduate Schools
Graduate School of Science and Technology
Department of Industrial Engineering
4. Theses(Ph.D)
설비 제어 특성을 이용 학습형 설비 예지 보전 지원 Framework 개발
- Author(s)
- QIN SHIMING
- Advisor
- 왕지남
- Department
- 일반대학원 산업공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2018-08
- Language
- eng
- Keyword
- Artificial neural network (ANN); PLC Log data analysis; electrical energy usage monitoring; fault detection and isolation; industrial process modelling; manufacturing systems; programmable logic controller (PLC)
- Alternative Abstract
- In modern factory maintenance department is one of most important departments. Maintenance work decide one factory’s production efficiency and the quality of the products, thereby influence the cost of products and the competitiveness of enterprises. There are many maintenance policy, but all researcher’s research direction focuses on prescient maintenance. But almost previous works using high performance measuring device to monitoring equipment health state, this kind of high performance device require dedicated communication line and it’s usually one-to-one monitoring. This determines previous framework is expensive and not easy to apply in large scale. In this paper describes one precognition maintenance framework which much different with previous studies. Proposed framework using controller(PLC) log data to determine the running state of the equipment, perception equipment health state through comprehensive analysis controller log data and energy consume data. The corresponding maintenance methods will recommend according to the relationship between the previous maintenance records and the health status of the equipment. Proposed framework supply one self-learning core to give one gradual improvement maintenance model for applied factory. Gradually improve predict accuracy rate to help maintenance manager make more applicable maintenance plan. When the framework runs for a long time, system will exactly predict maintenance time and required maintenance work. Efficiently decrease break-down time and maintenance cost.
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