Loading…
This event has ended. Visit the official site or create your own event on Sched.
Welcome to ICSV26!
Back To Schedule
Tuesday, July 9 • 12:30 - 12:50
DEEP LEARNING APPROACH CONSIDERING IMBALANCED DATA FOR HEALTH CONDITION MONITORING IN WIND TURBINE

Log in to save this to your schedule, view media, leave feedback and see who's attending!

Feedback form is now closed.
Vibration-based condition monitoring and fault diagnosis techniques are the keys to enhancing the reliability, safety and automation level of wind turbine systems. It has been recognized that the deep learning approaches are continuously achieving the state-of-the-art performance in this field. However, the actual restrictions, such as imbalanced fault dataset and low density in the sense of data value, prevent these approaches from being widely deployed in real wind turbine systems, since large sets of high-quality data are often required for effective training in deep learning approaches. To settle these problems, focal loss is introduced into deep learning for effectively discounting the effect of easy negatives. The vibration fault data of a wind turbine test rig are collected for case studies. The results prove that the proposed methodology is feasible and efficient, achieving high robustness and performance obtained from different data qualities.

Moderators
SL

Sébastien Laurier Chapleau

Senior Technical Specialist - Innovation - Dynamics & Acoustics, Bell (Flight)

Authors

Tuesday July 9, 2019 12:30 - 12:50 EDT
Westmount 3
  T10 Sig. Proc. & nonlin. mthds., RS03 Machinery health monit