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RDR-based knowledge based system to the failure detection in industrial cyber physical systems
journal contribution
posted on 2023-05-19, 18:25 authored by Kim, D, Han, SC, Lin, Y, Byeong KangByeong Kang, Lee, SCyber Physical System(CPS) allows to collect different sensor and alarm data from large number of facilities in industrial plants. Failure and faulty diagnosis is one of the most complicated and dynamic problems in the industrial plant management since most of failures are extremely ambiguous which needs to be solved based on an expert’s experience. This makes the solutions very subjective and requires too much time, efforts and monetary investment. In this paper, we are proposing new failure detection approach with machine learning and human expertise by using alarm data. As the first step of developing this new method, we collected several types of alarm data that detected functional failure in Hyundai Steel factory. We analyzed and processed the alarm data with 35 domain experts. Based on the data, we propose a knowledge based system which is Ripple Down Rule-based. This system acquires knowledge by machine learning which is maintained by human experts. The evaluation results showed that the proposed failure detection framework can reduce the time of human expertise acquisition and the cost of solving over-generalization and over-fitting problems by using machine learning techniques.
Funding
Ministry of Trade, Industry and Energy
History
Publication title
Knowledge-Based SystemsVolume
150Pagination
1-13ISSN
0950-7051Department/School
School of Information and Communication TechnologyPublisher
Elsevier Science BvPlace of publication
Po Box 211, Amsterdam, Netherlands, 1000 AeRights statement
Copyright 2018 Elsevier B.V.Repository Status
- Restricted