Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm

2016 ◽

Vol 9 (48) ◽

Cited By ~ 3

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Fault Diagnosis ◽

Decision Tree ◽

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Decision Tree Algorithm ◽

Vibration Signals ◽

Tree Algorithm

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Noise study for wind turbine blade vibration signals based on fast ICA algorithm

2015 8th International Congress on Image and Signal Processing (CISP) ◽

10.1109/cisp.2015.7408113 ◽

2015 ◽

Author(s):

Qian Zhao ◽

Wei Li ◽

Yichuan Shao ◽

Xingjia Yao ◽

Haonan Tian ◽

...

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Blade Vibration ◽

Vibration Signals ◽

Ica Algorithm

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

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/923/1/012009 ◽

2020 ◽

Vol 923 ◽

pp. 012009

Author(s):

A Joshuva ◽

M Arjun ◽

B Sujith Adhithya ◽

B Akash ◽

S Abdul Wahaab

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Vibration Signals ◽

Split Point

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Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study

Progress in Industrial Ecology An International Journal ◽

10.1504/pie.2019.10022055 ◽

2019 ◽

Vol 13 (3) ◽

pp. 232 ◽

Cited By ~ 4

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Data Mining ◽

Wind Turbine ◽

Turbine Blade ◽

Data Model ◽

Wind Turbine Blade ◽

Vibration Signals ◽

Selection Of

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Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

Structural Durability & Health Monitoring ◽

10.32604/sdhm.2019.03014 ◽

2019 ◽

Vol 13 (4) ◽

pp. 399-416 ◽

Cited By ~ 2

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Data Mining ◽

Comparative Study ◽

Wind Turbine ◽

Turbine Blade ◽

Condition Monitoring ◽

Wind Turbine Blade ◽

Vibration Signals ◽

Data Mining Approach

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Classification of wind turbine blade incidents regarding lightning risk management

2014 International Conference on Lightning Protection (ICLP) ◽

10.1109/iclp.2014.6973266 ◽

2014 ◽

Cited By ~ 2

Author(s):

Yoh Yasuda ◽

Toshiaki Fujii ◽

Kazuo Yamamoto ◽

Nobuyuki Honjo ◽

Shigeru Yokoyama

Keyword(s):

Risk Management ◽

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade

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Feature Vector Extraction and Classifier Design for State Classification of Wind Turbine Blade

The Journal of Korean Institute of Information Technology ◽

10.14801/jkiit.2021.19.6.89 ◽

2021 ◽

Vol 19 (6) ◽

pp. 89-97

Author(s):

Yong-Bin Kim ◽

In-Sik Choi

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Feature Vector ◽

Wind Turbine Blade ◽

Classifier Design ◽

State Classification

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A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features

Measurement ◽

10.1016/j.measurement.2019.107295 ◽

2020 ◽

Vol 152 ◽

pp. 107295

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Condition Monitoring ◽

Wind Turbine Blade ◽

Learning Approach ◽

Lazy Learning ◽

Vibration Signals

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A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study

ISA Transactions ◽

10.1016/j.isatra.2017.02.002 ◽

2017 ◽

Vol 67 ◽

pp. 160-172 ◽

Cited By ~ 23

Author(s):

A Joshuva. ◽

V Sugumaran.

Keyword(s):

Comparative Study ◽

Wind Turbine ◽

Turbine Blade ◽

Condition Monitoring ◽

Wind Turbine Blade ◽

Data Driven ◽

Vibration Signals ◽

Tree Algorithm ◽

Data Driven Approach

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Advances in wind turbine blade design and materials

10.1533/9780857097286 ◽

2013 ◽

Cited By ~ 18

Author(s):

Povl Brøndsted ◽

Rogier P. L. Nijssen

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Blade Design ◽

Turbine Blade Design

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Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm

Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm

Noise study for wind turbine blade vibration signals based on fast ICA algorithm

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

Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study

Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

Classification of wind turbine blade incidents regarding lightning risk management

Feature Vector Extraction and Classifier Design for State Classification of Wind Turbine Blade

A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study

Advances in wind turbine blade design and materials

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