Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study. (21st June 2019)

• Record Type: Journal Article
• Authors: Joshuva, A.; Sugumaran, V.
• Is Part Of: Progress in industrial ecology. Volume 13:Number 3(2019)
• Page Start: 232
• Page End: 251
• Publication Date: 2019-06-21
• Subjects: condition monitoring -- wind turbine blade -- histogram features -- machine learning -- meta classifiers -- vibration signals; Industrial ecology -- Periodicals; 658.4083
• Languages: English
• ISSNs: 1476-8917

Abstract

The modern developments in wind turbine fault diagnosis and condition monitoring are urged in recent times. This paper aims to identify different types of faults which occur on wind turbine blade as they are prone to vibration stress due to environmental and weather condition. The fault diagnosis problem was carried out using machine learning approach. This study was carried out using vibration sources which has been acquired from good and other fault condition blades using data acquisition system. From the recorded signals, histogram features were extracted and classified using meta classifiers. From the classifiers, a better data-model is suggested for a multi-class problem in wind turbine blade fault diagnosis.