期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 80, 期 -, 页码 31-44出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.04.019
关键词
Object-oriented Bayesian networks; Real-time; Fault diagnosis; Complex systems
资金
- Hong Kong Scholars Program [XJ2014004]
- National Natural Science Foundation of China [51309240]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130133120007]
- China Postdoctoral Science Foundation [2015M570624]
- Applied Basic Research Programs of Qingdao [14-2-4-68-jch]
- Science and Technology Project of Huangdao District [2014-1-48]
- Fundamental Research Funds for the Central Universities [14CX02197A]
Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes, but it requires modeling of large and complex systems, in situations such as fault diagnosis and reliability evaluation. Motivated by reduction of the overall complexities of BNs for fault diagnosis, and the reporting of faults that immediately occur, a real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using object-oriented Bayesian networks (OOBNs). The modeling methodology consists of two main phases: an off-line OOBN construction phase and an on-line fault diagnosis phase. In the off-line phase, sensor historical data and expert knowledge are collected and processed to determine the faults and symptoms, and OOBN-based fault diagnosis models are developed subsequently. In the on-line phase, operator experience and sensor real-time data are placed in the OOBNs to perform the fault diagnosis. According to engineering experience, the judgment rules are defined to obtain the fault diagnosis results. (C) 2016 Elsevier Ltd. All rights reserved.
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