Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme. Inturi, V., Shreyas, N., Chetti, K., & Sabareesh, G. R. Applied Acoustics, 174:107738, March, 2021. ![Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The current work reports a multi-level classification to envisage the location, type/category and severity level of local defects at different stages of speed in a wind turbine gearbox with minimal human intervention. Experiments are conducted by subjecting a three-stage gearbox to fluctuating speeds with multiple sensors recording the real-time information generated. Wavelet coefficients are employed to extract the statistical features from the raw signatures decomposed through wavelet transform. A decision tree algorithm is used to identify features of significance and an integrated multi-variable feature data set is devised based on feature-level data fusion. The intended multi-level classification on the integrated feature data set is accomplished with the help of machine-learning algorithms. The results reveal that the adaptive neuro-fuzzy inference system (ANFIS) performs the intended four-level classification on the wind turbine gearbox with a classification accuracy of 92%. Thus, the integration of multi-sensor information in conjunction with ANFIS as a classification algorithm, owing to its efficiency in predicting every possible detail about the health/condition of the different gearbox components, demonstrates its potential to be used as an adaptive condition monitoring as it.
@article{inturi_comprehensive_2021,
title = {Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme},
volume = {174},
issn = {0003-682X},
url = {https://www.sciencedirect.com/science/article/pii/S0003682X20308422},
doi = {10.1016/j.apacoust.2020.107738},
abstract = {The current work reports a multi-level classification to envisage the location, type/category and severity level of local defects at different stages of speed in a wind turbine gearbox with minimal human intervention. Experiments are conducted by subjecting a three-stage gearbox to fluctuating speeds with multiple sensors recording the real-time information generated. Wavelet coefficients are employed to extract the statistical features from the raw signatures decomposed through wavelet transform. A decision tree algorithm is used to identify features of significance and an integrated multi-variable feature data set is devised based on feature-level data fusion. The intended multi-level classification on the integrated feature data set is accomplished with the help of machine-learning algorithms. The results reveal that the adaptive neuro-fuzzy inference system (ANFIS) performs the intended four-level classification on the wind turbine gearbox with a classification accuracy of 92\%. Thus, the integration of multi-sensor information in conjunction with ANFIS as a classification algorithm, owing to its efficiency in predicting every possible detail about the health/condition of the different gearbox components, demonstrates its potential to be used as an adaptive condition monitoring as it.},
language = {en},
urldate = {2023-06-07},
journal = {Applied Acoustics},
author = {Inturi, Vamsi and Shreyas, N. and Chetti, Karthick and Sabareesh, G. R.},
month = mar,
year = {2021},
keywords = {ANFIS, Condition monitoring, Fault diagnosis, Multi-level classification, Wind turbine gearbox},
pages = {107738},
}
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Wavelet coefficients are employed to extract the statistical features from the raw signatures decomposed through wavelet transform. A decision tree algorithm is used to identify features of significance and an integrated multi-variable feature data set is devised based on feature-level data fusion. The intended multi-level classification on the integrated feature data set is accomplished with the help of machine-learning algorithms. The results reveal that the adaptive neuro-fuzzy inference system (ANFIS) performs the intended four-level classification on the wind turbine gearbox with a classification accuracy of 92%. 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