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 ◽

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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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 ◽

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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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 ◽

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Data Mining ◽

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Vibration Signals ◽

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A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study

Progress in Industrial Ecology An International Journal ◽

10.1504/pie.2018.10016959 ◽

2018 ◽

Vol 12 (1/2) ◽

pp. 14

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Wind Turbine ◽

Turbine Blade ◽

Condition Monitoring ◽

Moving Average ◽

Autoregressive Moving Average ◽

Learning Approach ◽

Vibration Signals ◽

Machine Learning Approach

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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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Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

Structural Durability & Health Monitoring ◽

10.32604/sdhm.2019.00287 ◽

2019 ◽

Vol 13 (2) ◽

pp. 181-203 ◽

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Machine Learning ◽

Data Mining ◽

Wind Turbine ◽

Turbine Blade ◽

Crack Detection ◽

Learning Algorithms ◽

Wind Turbine Blade ◽

Machine Learning Algorithms ◽

Data Mining Approach ◽

Detection And Localization

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A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study

Progress in Industrial Ecology An International Journal ◽

10.1504/pie.2018.095867 ◽

2018 ◽

Vol 12 (1/2) ◽

pp. 14 ◽

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Wind Turbine ◽

Turbine Blade ◽

Condition Monitoring ◽

Moving Average ◽

Autoregressive Moving Average ◽

Learning Approach ◽

Vibration Signals ◽

Machine Learning Approach

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Static Calibration of Microelectromechanical Systems (MEMS) Accelerometers for In-Situ Wind Turbine Blade Condition Monitoring

Special Topics in Structural Dynamics, Volume 6 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-3-319-15048-2_9 ◽

2015 ◽

pp. 91-98 ◽

Author(s):

O. O. Esu ◽

J. A. Flint ◽

S. J. Watson

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Condition Monitoring ◽

Microelectromechanical Systems ◽

Wind Turbine Blade ◽

Static Calibration ◽

Mems Accelerometers

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Comparative Study of the Effect of Chord Length Computation Methods in Design of Wind Turbine Blade

Lecture Notes in Electrical Engineering - Advanced Manufacturing and Automation IX ◽

10.1007/978-981-15-2341-0_14 ◽

2020 ◽

pp. 106-115

Author(s):

Temesgen Batu ◽

Hirpa G. Lemu

Keyword(s):

Comparative Study ◽

Wind Turbine ◽

Turbine Blade ◽

Chord Length ◽

Wind Turbine Blade

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A comparative study of RANS-based turbulence models for an upscale wind turbine blade

SN Applied Sciences ◽

10.1007/s42452-019-0254-5 ◽

2019 ◽

Vol 1 (3) ◽

Author(s):

Patrick Irungu Muiruri ◽

Oboetswe Seraga Motsamai ◽

Rehema Ndeda

Keyword(s):

Comparative Study ◽

Wind Turbine ◽

Turbine Blade ◽

Turbulence Models ◽

Wind Turbine Blade

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

Indian Journal of Science and Technology ◽

10.17485/ijst/2016/v9i48/107936 ◽

2016 ◽

Vol 9 (48) ◽

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Fault Diagnosis ◽

Decision Tree ◽

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Decision Tree Algorithm ◽

Vibration Signals ◽

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