Journal
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
Volume 22, Issue 5, Pages 472-482Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/09511920802537995
Keywords
NC machine tool; intelligent fault diagnosis; rough set; evidence theory
Categories
Funding
- National Natural Science Foundation of China [50675199]
- Science & Technology Products of Zhejiang Province [2006C11067]
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An intelligent fault diagnostic method was presented to satisfy the development requirements of next-generation intelligent NC machine tools. The framework of fault diagnosis unit was established first, which consisted of signal acquisition, diagnosis rule extraction and fault identification mechanism. The technique of diagnosis rule extraction was then studied and an algorithm for acquisition of decision rules was proposed. The algorithm simplified the analysis of core properties and unnecessary properties, and calculated reduction set by the backwards tracking approach. This algorithm reduced complexity in reductions calculation and improved the efficiency of rule extraction. Finally, to process failure data collected by various sensors, a fault identification mechanism using evidence theory was presented. Feasibility and practicability of the proposed method has been verified by the development and the preliminary application of a prototype system.
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