Journal of Intelligent Manufacturing

, Volume 17, Issue 1, pp 163–176 | Cite as

RMINE: A Rough Set Based Data Mining Prototype for the Reasoning of Incomplete Data in Condition-based Fault Diagnosis



Condition-based fault diagnosis aims at identifying the root cause of a system malfunction from vast amount of condition-based monitoring information and knowledge. The needs of extracting knowledge from vast amount of information have spurred the interest in data mining, which can be categorized into two stages data preparation and knowledge extraction. It has been established that most of the current data mining approaches to fault diagnosis focus on the latter stage. In reality, condition-based monitoring data may, most of the time, contain incomplete, or missing data, which have an adverse effect on the knowledge or diagnostic rules extracted. Several approaches to deal with missing data can be found in literature. Unfortunately, all of which have serious drawbacks. In this paper, a novel approach to the treatment of incomplete data is proposed for the data preparation stage, while a rough set based approach has been developed to pre-process the data for the extraction of diagnostic rules. The two-stage data mining technique is implemented into a prototype system, RMINE, which also possesses a self-learning ability to cope with the changing condition-based data. A real industrial case study of a pump system is used to demonstrate the fault diagnosis process using RMINE. The result has shown the potential of RMINE as a general data mining prototype to condition-based fault diagnosis.


Incomplete data rough set theory data mining fault diagnosis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Clark, P., Niblett, T. 1989The CN2 induction algorithmMachine Learning.3261283Google Scholar
  2. Fan, C. M., Guo, R. S., Chen, A., Hsu, K. C. and Wei, C. S. (2001) Data mining and fault diagnosis based on wafer acceptance test data and in-line manufacturing data. Proceedings of IEEE International Symposium on Semiconductor Manufacturing, pp. San Jose, CA, USA, 171–174.Google Scholar
  3. Greco, S., Matarazzo, B. and Slowinski, R. (1997) Rough set approach to multi-attribute choice and ranking problems. Proceedings of the Twelfth International Conference, pp. Hagen, Germany, 318–329.Google Scholar
  4. Greco, S., Matarazzo, B., Slowinski, R. 1999Rough approximation of a preference relation by dominance relationsEuropean Journal of Operational Research.1176383CrossRefGoogle Scholar
  5. Grzymala-Busse, J. W. (1991) On the unknown attribute values in learning from examples, in Methodologies for Intelligent Systems, Z.W. Ras, and M. Zemankova, (eds.), Springer-Verlag, Germany, pp. 368–377.Google Scholar
  6. Hashmi, R.R., Blanc, L.A., Rucks, C.T., Rajaratnam, A. 1998A hybrid intelligent system for predicting bank holding structuresEuropean Journal of Operational Research.109390402Google Scholar
  7. Kamrani, A., Rong, W., Gonzalez, R. 2001A genetic algorithm methodology for data mining and intelligent knowledge acquisitionComputers & Industrial Engineering.40361377CrossRefGoogle Scholar
  8. Khoo, L.P., Tor, S.B., Li, J.R. 2001A rough set approach to the ordering of basic events in a fault tree for fault diagnosisInternational Journal of Advanced Manufacturing Technology.17769774Google Scholar
  9. Khoo, L.P., Tor, S.B., Zhai, L.Y. 1999A rough-set based approach for classification and rule inductionInternational Journal of Advanced Manufacturing Technology.15438444Google Scholar
  10. Kusiak, A. 2000Computational Intelligence in Design and ManufacturingJohn Wiley & Sons Inc.New YorkGoogle Scholar
  11. Kusiak, A. 2001Rough set theory: a data mining tool for semiconductor manufacturingIEEE Transactions on Electronics Packaging Manufacturing.244450CrossRefGoogle Scholar
  12. Pawlak, Z. 1982Rough setsInternational Journal of Information and Computer Science.11348356Google Scholar
  13. Pawlak, Z. 1991Rough Set: Theoretical Aspects of Reasoning about DataKluwer Academic PublishersDordrechtGoogle Scholar
  14. Pawlak Z. (1996). Why rough sets? Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, Piscataway, New Jersey, USA, Vol. 2, pp. 738–743.Google Scholar
  15. Quinlan J.R. (1986). The effect of noise on concept learning, in Machine Learning: An Artificial Intelligent Approach, R. Michalski, J. Carbonell, and T. Mitchell, (eds.), San Mateo, CA. Morgan Kauffman Inc, 2, pp. 149–66.Google Scholar
  16. Tsumoto, S. (1998) Knowledge discovery in medical databases based on rough sets and Attribute-oriented Generalisation. Proceedings of IEEE World Conference on Fuzzy Systems, Anchorage, USA, Vol. 2, pp. 1296–1301.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Autodesk Vault R&D team (Singapore), Manufacturing Solutions DivisionAutodesk Asia Pte LtdSingapore
  2. 2.Division of Mechatronics and Design, School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingapore
  3. 3.Division of Manufacturing Engineering, School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations