Kohonen Neural Networks for Machine and Process Condition Monitoring

  • Tom Harris


Kohonen feature map neural networks have been used for a variety of successful applications since their introduction in the late eighties. They have one major advantage over their more common peers in that they are capable of unsupervised learning. This property makes them ideal for machine health monitoring situations.

Unsupervised learning allows the network to represent single or multiple classes according to distribution and density. Novelty detection is then possible on-line or classes can be labelled to give diagnosis. This presentation explains the special nature of machine monitoring applications in data availability and desired diagnosis information and provides examples of such systems working in different environments.


Unsupervised Learning Novelty Detection Predictive Maintenance Late Eighty Neural Network Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • Tom Harris
    • 1
  1. 1.Neural Applications GroupBrunel UniversityEgham, SurreyUK

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