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 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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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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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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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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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 ◽

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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 ◽

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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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Reliability Analysis of Condition Monitoring Network of Wind Turbine Blade Based on Wireless Sensor Networks

IEEE Transactions on Sustainable Energy ◽

10.1109/tste.2018.2836664 ◽

2019 ◽

Vol 10 (2) ◽

pp. 549-557 ◽

Author(s):

Zhixin Fu ◽

Yang Luo ◽

Chenghong Gu ◽

Furong Li ◽

Yuan Yue

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Wind Turbine ◽

Reliability Analysis ◽

Turbine Blade ◽

Condition Monitoring ◽

Monitoring Network ◽

Wind Turbine Blade ◽

Wireless Sensor

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