Split-Point and Attribute-Reduced Classifier Approach for Fault Diagnosis of Wind Turbine Blade through Vibration Signals

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...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-09, Vol.923 (1), p.12009
Main Authors: Joshuva, A, Arjun, M, Adhithya, B Sujith, Akash, B, Wahaab, S Abdul
Format: Article
Language:English
Subjects:
10-fold cross classification
Accelerometers
Classifiers
Data processing
Fault diagnosis
Feature extraction
J48
Machine learning
Piezoelectricity
Pitch (inclination)
Signal processing
SPAARC
statistical
Turbine blade fault
Turbine blades
Vibration analysis
Wind turbines
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Summary: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.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/923/1/012009