A Data-Driven Approach to Detect Mechanical Faults in Wind Turbine Gearbox
Abstract
Online condition monitoring systems play an important role in preventing catastrophic failure, reducing maintenance costs, and improving the system reliability. In this paper, wind turbine gearbox mechanical fault detection system is developed. An adaptive filtering technique is applied to separate the impulsive components from the periodic components of the vibration signals. Then different features of the periodic components and impulsive components are extracted. An extreme learning machine based classifier is designed and trained by using the features extracted from simulated vibration data of wind turbine gearbox. Simulated vibration signals of wind turbines gearbox are used to demonstrate the effectiveness of the presented methodology.
Recommended Citation
L. Li and Z. Sun, "A Data-Driven Approach to Detect Mechanical Faults in Wind Turbine Gearbox," Proceedings of the ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing (2017, Los Angeles, CA), vol. 3, American Society of Mechanical Engineers (ASME), Jun 2017.
The definitive version is available at https://doi.org/10.1115/MSEC2017-2736
Meeting Name
ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 (2017: Jun. 4-8, Los Angeles, CA)
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Adaptive Filtering; Fault Diagnostics; Statistical Features
International Standard Book Number (ISBN)
978-079185074-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
01 Jun 2017
