Journal
IEEE TRANSACTIONS ON RELIABILITY
Volume 59, Issue 4, Pages 700-705Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2010.2083231
Keywords
Gearbox; health condition; prediction; recurrent neural network
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Funding
- Natural Sciences and Engineering Research Council of Canada
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The development of accurate health condition prediction approaches has been a key research topic in condition based maintenance (CBM) in recent years. However, current health condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. Neural network based methods have been considered to be a very promising category of methods for equipment health condition prediction. In this paper, we propose a neural network prediction model called extended recurrent neural network (ERNN). An ERNN based approach is developed for health condition prediction of gearboxes based on the vibration data collected from a gearbox experimental system. The results demonstrate the capability of the ERNN based approach for producing satisfactory health condition prediction results. A comparative study based on the gearbox experiment data further establishes ERNN as an effective recurrent neural network model for equipment health condition prediction.
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