Fault Prognosis of Mechanical Components Using On-Line Learning Neural Networks

  • David Martínez-Rego
  • Oscar Fontenla-Romero
  • Beatriz Pérez-Sánchez
  • Amparo Alonso-Betanzos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6352)


Predictive maintenance of industrial machinery has steadily emerge as an important topic of research. Due to an accurate automatic diagnosis and prognosis of faults, savings of the current expenses devoted to maintenance can be obtained. The aim of this work is to develop an automatic prognosis system based on vibration data. An on-line version of the Sensitivity-based Linear Learning Model algorithm for neural networks is applied over real vibrational data in order to assess its forecasting capabilities. Moreover, the behavior of the method is compared with that of an efficient and fast method, the On-line Sequential Extreme Learning Machine. The accurate predictions of the proposed method pave the way for future development of a complete prognosis system.


Root Mean Square Hide Neuron Mechanical Component Vibration Data Predictive Maintenance 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David Martínez-Rego
    • 1
  • Oscar Fontenla-Romero
    • 1
  • Beatriz Pérez-Sánchez
    • 1
  • Amparo Alonso-Betanzos
    • 1
  1. 1.Laboratory for Research and Development in Artificial Intelligence (LIDIA), Department of Computer Science, Faculty of InformaticsUniversity of A CoruñaA CoruñaSpain

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