Knowledge Acquisition of Spindle Bearings Fault Based on Rough Sets

Abstract:

An identification method of spindle bearing fault based on rough sets theory is proposed in the article. By collecting bearing’s typical fault signal and using signal information processing techniques, vibration fault data is obtained. Then, equidistant clustering analysis method is introduced into discretization of experimental data of continuous attributes. In this way, vibration fault data table meets the requirement of rough sets data analysis. Besides, attribute importance algorithm is used in order to realize the reduction of condition attribute in the decision table. Thus, fault information which hidden in huge signal data is extracted. Therefore, simple and clear fault pattern rules are acquired. The result indicates that the method can realize fault pattern identification of spindle’s bearings and it is of great application value in practical fault pattern identification.

• Periodical: Advanced Materials Research (Volumes 503-504)
• Pages: 1133-1136
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.503-504.1133
• Online since: April 2012
• Authors: Xiao Zheng Xie, Rong Zhen Zhao, Li Yang, Yun Ping Yao
• Keywords: Bearings, Decision Table, Fault, Knowledge Acquisition, Rough Set (RS)

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