Abstract
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.
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Wang, M., Hu, N.Q., Yang, S.F., Qin, G.J.: Study on application of fault simulation based fault-knowledge base. J. Astronaut. 31(4), 1253–1258 (2010) (in Chinese)
Zadeh, L.A.: Fuzzy sets and information granularity. In: Gupta, M., Ragade, R.K., Yager, R.R. (eds.) Advances in Fuzzy Set Theory and Applications. North-Holland Publishing Company (1979)
Lin, T.Y.: Granular computing. In: Announcement of the BISC Special Interest Group on Granular Computing (1997)
Zhang, Z.S., Yan, X.X., Cheng, W.: Granular computing with application to fault diagnosis. J. Xi’an Jiaotong Univ. 43(9), 37–41 (2009) (in Chinese)
Li, F., Xue, J., Xie, K.M.: Granular computing theory in the application of fault diagnosis. In: 2008 Chinese Control and Decision Conference (CCDC 2008), pp. 595–597 (2008) (in Chinese)
Cao, Y.F., Wang, Y.C., Wang, J.W.: Tolerant neural network fault diagnosis system based on rough set. Comput. Eng. Des. 27, 637–639 (2006)
Zhu, Z.Q., Hu, C.: A new commix intelligent fault diagnosis method based on rough set theory. Control Decis. 21, 233–235 (2006)
Yager, R.R., Filev, D.: Operations for granular computing: mixing words with numbers. In: Proceedings of 1998 IEEE International Conference on Fuzzy Systems, pp. 123–128 (1998)
Pawlak, Z.: Granularity of knowledge, indiscernibility and rough sets. In: Proceedings of 1998 IEEE International Conference on Fuzzy Systems, pp. 106–110 (1998)
Yao, Y.Y.: Granular Computing: past, present and future. In: Proceedings of the 2008 IEEE International Conference on Granular Computing, pp. 80–85 (2008)
Yao, Y.Y.: Perspectives of granular computing. In: IEEE International Conference on Granular Computing, pp. 85–90, Beijing, China (2005)
Ma, M.J., Zhang, W.X., Li, T.A.: A covering model of granular computing. In: The 2005 International Comference on Machine Learning and Cybernetics, pp. 1625–1630, Guangzhou, China (2005)
Grzymala-Busse, J.W.: Rough Set Approach to Incomplete Data. In: ICAISC 2004, pp. 50–55 (2004)
Grzymala-Busse, J.W.: Characteristic relations for incomplete data: A generalization of the indiscernibility re1ation. In: Proceeding of the 3rd International Conference on Rough Sets and Current Trends in Computing, pp. 244–253 (2004)
Grzymala-Busse, J.W., Siddhaye, S.: Rough Set approaches to rule induction from incomplete data. In: Proceedings of the IPMU’2004, the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 923–930 (2004)
Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
Polkowski, L., Skowron, A.: Towards adaptive calculus of granules. In: Proceedings of 1998 IEEE International Conference on Fuzzy Systems, pp. 111–116 (1998)
Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 19, 111–127 (1997)
Liu, Q., Jin, W.B.: Clustering research using dynamic modeling based on granular computing. In: 2005 IEEE International Conference on Granular Computing, pp. 539–543 (2005)
Hu, F., Huang, H., Wang, G.Y.: Granular computing in incomplete information system. Mini-Micro Syst. 26(8), 1335–1339 (2005) (in Chinese)
Hu, Q.H., Yu, D.R., Xie, Z.X.: Information preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 31, 414–423 (2006) (in Chinese)
Duntsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artif. Intell. 106, 109–137 (1998)
Huang, W.T., Wang, W.J., Zhao, X.Z., Meng, Q.X.: Extracting rules for fault diagnosis from incomplete data based on discernibility matrix primitive. J. Mech. Eng. 45(9), 46–51 (2009) (in Chinese)
Kryszkiewicz, M.: Rules in incomplete information systems. Info. Sci. 113, 271–292 (1999)
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Wang, M., Hu, Nq. & Qin, Gj. A Method for Rule Extraction Based on Granular Computing: Application in the Fault Diagnosis of a Helicopter Transmission System. J Intell Robot Syst 71, 445–455 (2013). https://doi.org/10.1007/s10846-012-9793-3
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DOI: https://doi.org/10.1007/s10846-012-9793-3