Fuzzy condition monitoring of recirculation fans and filters

  • Mike GerdesEmail author
  • Diego Galar
Original Article


A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.


Fuzzy decision trees Post-fuzzyfication Condition monitoring Aircraft 


  1. Apte C, Damerau F, Weiss SM, Apte C, Damerau F, Weiss S (1998) Text mining with decision trees and decision rules. In: Proceedings of the conference on automated learning and discovery, workshop 6: learning from text and the webGoogle Scholar
  2. Chiang I-J, Hsu JY-J (2002) Fuzzy classification trees for data analysis. Fuzzy Sets Syst. 130(1):87–99MathSciNetCrossRefzbMATHGoogle Scholar
  3. Friedl MA, Brodley CE (1997) Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 61(3):399–409CrossRefGoogle Scholar
  4. Gerdes M, Scholz D (2011) Parameter optimization for automated signal analysis for condition monitoring of aircraft systems. In 3rd international workshop on Aircraft System Technologies, AST 2011 (TUHH, Hamburg, 31. March - 01. April 2011)Google Scholar
  5. Janikow CZ (1995) A genetic algorithm for optimizing fuzzy decision trees. In: Proceedings of the 6th International Conference on Genetic Algorithms, San FranciscoGoogle Scholar
  6. Mierswa I, Morik K (2005) Automatic feature extraction for classifying audio data. Mach Learn 58(2–3):127–149CrossRefzbMATHGoogle Scholar
  7. Quinlan JR (1986) Induction of decision trees. Mach. Learn 1(1):81–106Google Scholar
  8. Quinlan J (1987) Decision trees as probabilistic classifiers. In: Langley P (ed) Proceedings of the fourth international workshop on machine learning, Morgan Kaufmann, pp. 31–37Google Scholar
  9. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San FranciscoGoogle Scholar
  10. Rabiner L, Juang BH (1993) Fundamentals of speech recognition. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  11. Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach. Pearson Education, Upper Saddle RiverzbMATHGoogle Scholar
  12. Saimurugan M, Ramachandran K, Sugumaran V, Sakthivel N (2011) Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Syst Appl 38(4):3819–3826CrossRefGoogle Scholar
  13. Sakthivel N, Sugumaran V, Nair BB (2010) Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mech Syst Signal Process 24(6):1887–1906CrossRefGoogle Scholar
  14. Schmid H (1994) Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the international conference on new methods in language processingGoogle Scholar
  15. Sugumaran V, Ramachandran K (2011) Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Syst Appl 38(4):4088–4096CrossRefGoogle Scholar
  16. U. o. Waikato, WEKA, 2009Google Scholar
  17. Wang X-Z, Zhai J-H, Lu S-X (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202MathSciNetCrossRefzbMATHGoogle Scholar
  18. Yuan Y, Shaw MJ (1995) Induction of fuzzy decision trees. Fuzzy Sets Syst 69(2):125–139MathSciNetCrossRefGoogle Scholar

Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2016

Authors and Affiliations

  1. 1.Aero - Aircraft Design and Systems GroupHamburg University of Applied SciencesHamburgGermany
  2. 2.Division of Operation and Maintenance EngineeringLuleå University of TechnologyLuleåSweden
  3. 3.Reliability and Maintenance EngineeringUniversity of SkövdeSkövdeSweden

Personalised recommendations