Fault prognosis of mechanical components using on-line learning neural networks

Authors:
David Martínez-Rego;Óscar Fontenla-Romero;Beatriz Pérez-Sánchez;Amparo Alonso-Betanzos
Affiliations:
Laboratory for Research and Development in Artificial Intelligence, Department of Computer Science, Faculty of Informatics, University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Department of Computer Science, Faculty of Informatics, University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Department of Computer Science, Faculty of Informatics, University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Department of Computer Science, Faculty of Informatics, University of A Coruña, A Coruña, Spain
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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Abstract

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 LearningMachine. The accurate predictions of the proposed method pave the way for future development of a complete prognosis system.