Abstract
This study proposes a data processing and analysis of wind turbine blade faults using split-point and attribute-reduced classifier (SPAARC) through statistical-machine learning approach. In this study, the fault like erosion, hub-blade loose connection, pitch angle twist, bend and crack faults have been simulated and the vibration data has been taken using a piezoelectric accelerometer. With the recorded data, statistical features where extracted and with the extracted features were used to classify the fault condition on the wind turbine blade through SPAARC. The classification accuracy was found to be 85.67% and validated through 10-fold-cross-validation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
