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Title: A survey of knowledge-based intelligent fault diagnosis techniques
Authors: Xu, S 
Issue Date: 2019
Source: Journal of physics. Conference series, 2019, v. 1187, 32006, p. 1-6
Abstract: With the development of information technologies, more and more real-time data can be obtained from production and operation process. Thus, how to extract effective information from these massive data, so as to carry out in-depth statistics and mining of faults, and gradually explore the faults laws and causes are crucial for intelligent factories. In recent years, a variety of statistical learning and data analysis methods have been used in fault diagnosis. Due to the complex structure, multi-source failure and suddenness of the industrial production system, the combination of empirical knowledge and mechanism principles can solve various fault problems. This paper summarizes several commonly used fault diagnosis methods, and focuses on knowledge-based intelligent fault diagnosis, including first-order logic knowledge representation method, production knowledge representation method, framework knowledge representation method, object-oriented knowledge representation method and Semantic-based knowledge representation methods.
Publisher: Institute of Physics Publishing
Journal: Journal of physics. Conference series 
ISSN: 1742-6588
EISSN: 1742-6596
DOI: 10.1088/1742-6596/1187/3/032006
Description: International Symposium on Power Electronics and Control Engineering (ISPECE), Xi'an University of Technology, Xi'an, People's Republic of China, Dec 28-30, 2018
Rights: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (https://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.
The following publication Xu, S. C. (2019). A survey of knowledge-based intelligent fault diagnosis techniques. Journal of Physics. Conference Series, 1187, 32006, 1-6 is available at https://dx.doi.org/10.1088/1742-6596/1187/3/032006
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