Advanced Forecasting and Classification Technique for Condition Monitoring of Rotating Machinery

  • Ilya Mokhov
  • Alexey Minin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


Prediction and classification of particular faults in rotating machinery, based on a given set of measurements, could significantly reduce the overall costs of maintenance and repair. Usually the vibration signal is sampled with a very high frequency due to its nature, thus it is quite difficult to do considerably long forecasting based on the methods, which are suitable for e.g. financial time series (where the sampling frequency is smaller). In this paper new forecasting and classification technique for particular vibration signal characteristics is proposed. Suggested approach allows creating a part of control system responsible for early fault detection, which could be used for preventive maintenance of industrial equipment. Presented approach can be extended to high frequency financial data for the prediction of “faults” on the market.


fault analysis and prevention artificial neural networks artificial intelligence rotating machinery ball bearing failures predictive monitoring 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ilya Mokhov
    • 1
  • Alexey Minin
    • 2
  1. 1.Saint-Petersburg State University, Applied Mathematics and Control Processes 
  2. 2.Saint-Petersburg State University, Applied Computational Physics 

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