On the use of AI based vibration condition monitoring of wind turbine gearboxes. (May 2019)

• Record Type: Journal Article
• Authors: Koukoura, Sofia; Carroll, James; McDonald, Alasdair
• Is Part Of: Journal of physics. Volume 1222(2019)
• Publication Date: 2019-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1222/1/012045
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Condition monitoring (CM) systems are installed in wind turbines (WTs) in order to avoid component downtime and reduce maintenance costs. Vibration monitoring is widely used for the WT gearbox, which is a component with a significant downtime. Given that the installed wind capacity grows, the volume of CM data increases, making manual interpretation of vibration signals challenging. Therefore, there is a need for an efficient and automated maintenance decision support system. The aim to this paper is to propose an automated framework for gearbox incipient failure diagnosis. The framework utilises vibration signals and performs health estimation and fault isolation based on signal processing and artificial intelligence (AI) techniques. The methodology is demonstrated through a case study of vibration data from operating WTs with known gearbox failures. The study can be used to optimise wind turbine maintenance actions.