Problems of declarative and procedural knowledge acquisition for machinery diagnostics

  • Wojciech Moczulski Silesian University of Technology

Abstract

The paper deals with selected problems of knowledge acquisition for intelligent information systems that may be applied for aiding technical diagnostics of machinery and equipment. Two main kinds of knowledge are discussed, i.e. declarative and procedural knowledge. Some methods of declarative knowledge acquisition from domain experts and from databases are presented, the latter being divided into machine learning methods and knowledge discovery ones. Examples of declarative knowledge acquisition and discovery from databases are shown. Moreover, an example of procedural knowledge acquisition from a domain expert is presented. The paper concludes with new issues of knowledge acquisition methodology.

Keywords

intelligent information systems, knowledge base, procedural knowledge, declarative knowledge, knowledge acquisition, knowledge discovery,

References

[1] B.G. Buchanan, et al. Constructing an expert system. In: F. Hayes-Roth, D.A. Waterman, eds., Building Expert Systems, 127-168. Addison-Wesley, Reading, MA, 1983.
[2] W. Cholewa, J. Kaźmierczak. Data Processing and Reasoning in Technical Diagnostics. WNT, Warszawa, 1995.
[3] W. Cholewa, J . Kicińki, eds. Machinery Diagnostics. Inverted Diagnostic Models (in Polish). Silesian University of Technology, Gliwice, 1997.
[4] K. Ciupke. Selection and reduction of information in machinery diagnostics (in Polish). Ph.D. thesis, Silesian University of Technology, Department of Fundamentals of Machinery Design, Gliwice, 2001.
[5] J. Dietrych. System and Design (in Polish). WNT, Warszawa, 1985.
Published
Feb 22, 2023
How to Cite
MOCZULSKI, Wojciech. Problems of declarative and procedural knowledge acquisition for machinery diagnostics. Computer Assisted Methods in Engineering and Science, [S.l.], v. 9, n. 1, p. 71-86, feb. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/1141>. Date accessed: 21 nov. 2024.
Section
Articles