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@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},
}