A Method for Rule Extraction Based on Granular Computing: Application in the Fault Diagnosis of a Helicopter Transmission System

How to extract decision rules from incomplete decision table is of importance in fault diagnosis of helicopter transmission system. This paper introduces a knowledge acquisition method based on Granular Computing (GrC) for fault diagnosis of helicopter transmission system. First, following semantic analysis of missing attribute values in decision table, the basic idea of construction and interpretation of granules based on characteristic relation is studied. Then, the definition of GrC model based on characteristic relation as well as its construction algorithm is developed. Thus, a set of granules can be obtained completely and its implied information is consistent with the original decision table. Subsequently, the algorithm of attribute reduction in GrC is proposed. According to the definition of generalized decision rule, the way of extracting optimal decision rule from granules is studied. At last, Combined with an incomplete decision table for fault diagnosis of transmission system, this method has been achieved, and the analysis result shows its validity.

[1]  Wentao Huang.  Extracting Rules for Fault Diagnosis from Incomplete Data Based on Discernibility Matrix Primitive , 2009 .

[2]  Z. Pawlak.  Granularity of knowledge, indiscernibility and rough sets , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[3]  Wu Yu.  Granular Computing in Incomplete Information Systems , 2005 .

[4]  Qinghua Hu,et al.  Information-preserving hybrid data reduction based on fuzzy-rough techniques , 2006, Pattern Recognit. Lett..

[5]  Jerzy W. Grzymala-Busse,et al.  Rough Set Approach to Incomplete Data , 2004, ICAISC.

[6]  Wen-Xiu Zhang,et al.  A covering model of granular computing , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[7]  Ivo Diintsch.  Uncertainty measures of rough set prediction , 2003 .

[8]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[9]  Jerzy W. Grzymala-Busse,et al.  Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation , 2005, Trans. Rough Sets.

[10]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[11]  R. Yager,et al.  Operations for granular computing: mixing words and numbers , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[12]  Marzena Kryszkiewicz,et al.  Rules in Incomplete Information Systems , 1999, Inf. Sci..

[13]  Yiyu Yao,et al.  Granular Computing , 2008 .

[14]  A. Skowron,et al.  Towards adaptive calculus of granules , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[15]  Cheng Wei.  Granular Computing with Application to Fault Diagnosis , 2009 .

[16]  Qin Guo-jun.  Study on Application of Fault-Simulation-Based Fault-Knowledge Base , 2010 .

[17]  Yiyu Yao,et al.  Granular Computing: Past, Present, and Future , 2008, Rough Sets and Knowledge Technology.

[18]  Vijay K. Rohatgi,et al.  Advances in Fuzzy Set Theory and Applications , 1980 .

[19]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[20]  J. Grzymala-Busse,et al.  Rough Set Approaches to Rule Induction from Incomplete Data , 2004 .

[21]  Feng Li,et al.  Granular computing theory in the application of fault diagnosis , 2008, 2008 Chinese Control and Decision Conference.

[22]  Qun Liu,et al.  Clustering research using dynamic modeling based on granular computing , 2005, 2005 IEEE International Conference on Granular Computing.

[23]  Yiyu Yao,et al.  Perspectives of granular computing , 2005, 2005 IEEE International Conference on Granular Computing.