Abstract
Rough set is a new data analysis theory. It is often used to deal with fuzzy and uncertain problems. Attribution reduction is the key step in obtaining the knowledge by utilizing rough set. An improved heuristic reduction algorithm of attribute significance is proposed in the study based on analyzing the classic knowledge acquisition method of rough set theory. The algorithm corrects the discernibility matrix and redefines the calculation method of attribute importance. Then it fuses the both, gets the core by using the revised method of discernibility matrix and calculates the attribute importance by using the weighted method and then the algorithm is applied to extract the rules of the hoist fault diagnosis. Verified by the experiment, using the algorithm, it can excavate high reliability diagnosis rules from existing history diagnosis knowledge and expert knowledge. This method can provide reasonable basis for fault diagnosis.
Highlights
Using reasoning and judgment method, Mechanical equipment fault diagnosis system can diagnose the fault accurately and effectively, the key factor to form a fault diagnosis system is a complete knowledge base [1]
Knowledge acquisition problem is recognized as the “bottleneck” problem in fault diagnosis system [2, 3]
Ref. [10] studied the method of fuzzy rules extraction based on rough set in depth, proposed a new heuristic reduction algorithm based on fuzzy membership
Summary
Using reasoning and judgment method, Mechanical equipment fault diagnosis system can diagnose the fault accurately and effectively, the key factor to form a fault diagnosis system is a complete knowledge base [1]. [10] studied the method of fuzzy rules extraction based on rough set in depth, proposed a new heuristic reduction algorithm based on fuzzy membership It corrected the fuzzy membership function, it was applied into the chemical process fault diagnosis and the validity of the method was verified. In this study, the existing classic rough set algorithm was improved and fused and a new fault diagnosis knowledge acquisition method was put forward based on the theory of rough set. It can find useful knowledge patterns from data and generate the diagnostic rules. These rules provides a strong theoretical and data support for fault diagnosis
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