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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, S
Cyber 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 Systems

Volume

150

Pagination

1-13

ISSN

0950-7051

Department/School

School of Information and Communication Technology

Publisher

Elsevier Science Bv

Place of publication

Po Box 211, Amsterdam, Netherlands, 1000 Ae

Rights statement

Copyright 2018 Elsevier B.V.

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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