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},
}
Downloads: 0
{"_id":"GC3SxxWXsdZY6AwxQ","bibbaseid":"inturi-shreyas-chetti-sabareesh-comprehensivefaultdiagnosticsofwindturbinegearboxthroughadaptiveconditionmonitoringscheme-2021","author_short":["Inturi, V.","Shreyas, N.","Chetti, K.","Sabareesh, G. R."],"bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Inturi"],"firstnames":["Vamsi"],"suffixes":[]},{"propositions":[],"lastnames":["Shreyas"],"firstnames":["N."],"suffixes":[]},{"propositions":[],"lastnames":["Chetti"],"firstnames":["Karthick"],"suffixes":[]},{"propositions":[],"lastnames":["Sabareesh"],"firstnames":["G.","R."],"suffixes":[]}],"month":"March","year":"2021","keywords":"ANFIS, Condition monitoring, Fault diagnosis, Multi-level classification, Wind turbine gearbox","pages":"107738","bibtex":"@article{inturi_comprehensive_2021,\n\ttitle = {Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme},\n\tvolume = {174},\n\tissn = {0003-682X},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0003682X20308422},\n\tdoi = {10.1016/j.apacoust.2020.107738},\n\tabstract = {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.},\n\tlanguage = {en},\n\turldate = {2023-06-07},\n\tjournal = {Applied Acoustics},\n\tauthor = {Inturi, Vamsi and Shreyas, N. and Chetti, Karthick and Sabareesh, G. R.},\n\tmonth = mar,\n\tyear = {2021},\n\tkeywords = {ANFIS, Condition monitoring, Fault diagnosis, Multi-level classification, Wind turbine gearbox},\n\tpages = {107738},\n}\n\n\n\n\n\n\n\n","author_short":["Inturi, V.","Shreyas, N.","Chetti, K.","Sabareesh, G. R."],"key":"inturi_comprehensive_2021","id":"inturi_comprehensive_2021","bibbaseid":"inturi-shreyas-chetti-sabareesh-comprehensivefaultdiagnosticsofwindturbinegearboxthroughadaptiveconditionmonitoringscheme-2021","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0003682X20308422"},"keyword":["ANFIS","Condition monitoring","Fault diagnosis","Multi-level classification","Wind turbine gearbox"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["anfis","condition monitoring","fault diagnosis","multi-level classification","wind turbine gearbox"],"search_terms":["comprehensive","fault","diagnostics","wind","turbine","gearbox","through","adaptive","condition","monitoring","scheme","inturi","shreyas","chetti","sabareesh"],"title":"Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme","year":2021}