Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction

Rough set has been shown to be a valuable approach to mine rules from a remote monitoring manufacturing process. In this research, an application of the fuzzy set theory with the fuzzy variable precision rough set approach for mining the causal relationship rules from the database of a remote monitoring manufacturing process is presented. The membership function in the fuzzy set theory is used to transfer the data entries into fuzzy sets, and the fuzzy variable precision rough set approach is applied to extract rules from the fuzzy sets. It is found that the induced rules are identical to the practical knowledge and fault diagnosis thinking of human operators. The induced rules are then compared with the rules induced by the original rough set approach. The comparison shows that the rules induced by the fuzzy rough set are expressed in linguistic forms, and are evaluated by plausibility and future effectiveness measures. The fuzzy rough set approach, being less sensitive to noisy data, induces better rules than the original rough set approach.

[1]  M. Beynon,et al.  Variable precision rough set theory and data discretisation: an application to corporate failure prediction , 2001 .

[2]  Andrzej Skowron,et al.  Rough Sets , 1995, Lecture Notes in Computer Science.

[3]  T. Hong,et al.  Learning a coverage set of maximally general fuzzy rules by rough sets , 2000 .

[4]  Donald H. Kraft,et al.  Vocabulary mining for information retrieval: rough sets and fuzzy sets , 2001, Inf. Process. Manag..

[5]  Malcolm J. Beynon,et al.  Reducts within the variable precision rough sets model: A further investigation , 2001, Eur. J. Oper. Res..

[6]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[7]  Tzung-Pei Hong,et al.  Knowledge acquisition from quantitative data using the rough-set theory , 2000, Intell. Data Anal..

[8]  David A. Bell,et al.  Computational methods for rough classification and discovery , 1998, KDD 1998.

[9]  Yiyu Yao,et al.  Data mining using extensions of the rough set model , 1998, KDD 1998.

[10]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[11]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[12]  Liangsheng Qu,et al.  Fault diagnosis using Rough Sets Theory , 2000 .

[13]  Li Lin,et al.  Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets , 2003, J. Intell. Manuf..

[14]  Andrew Kusiak,et al.  Computational Intelligence in Design and Manufacturing , 2000 .

[15]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .