Artificial Immune Systems for Data Classification in Planetary Gearboxes Condition Monitoring

  • Edyta Brzychczy
  • Piotr Lipiński
  • Radoslaw Zimroz
  • Patryk Filipiak
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


In the paper a problem of diagnostic data classification is discussed. The classic condition monitoring approach requires two examples of machines: one in a good and one in a bad condition. From the industrial perspective such a requirement is often very difficult to fulfill, especially in the case of machines with an unique design. To overcome it, we proposed to use the Artificial Immune System (AIS) based approach to classify multidimensional diagnostic data. AIS allows to recognize a change of the machine condition based on a training phase using the dataset related to a good condition. To validate the proposed procedure and assess efficiency of the condition recognition, an extra data set from another machine (of the same type) in a bad condition was used. In the paper several key issues related to the selection of parameters have been discussed.


Planetary gearbox Condition monitoring Non-stationary operations Multidimensional symptom space Artificial immune systems Classification 



This work is partially supported by the statutory grant No. S20096 (R. Zimroz).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Edyta Brzychczy
    • 1
  • Piotr Lipiński
    • 2
  • Radoslaw Zimroz
    • 3
  • Patryk Filipiak
    • 2
  1. 1.Faculty of Mining and GeoengineeringAGH University of Science and TechnologyCracowPoland
  2. 2.Institute of Computer ScienceUniversity of WroclawWroclawPoland
  3. 3.Diagnostics and Vibro-Acoustics Science LaboratoryWroclaw University of TechnologyWroclawPoland

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