Multi-component fault classification of a wind turbine gearbox using integrated condition monitoring and hybrid ensemble method approach
Authors:
- S V V S Narayana PichikaSPS V V S Narayana Pichika
- Godhala MeganaaGMGodhala Meganaa
- Sabareesh Geetha RajasekharanSRSabareesh Geetha Rajasekharan
- Aruna MalapatiAMAruna Malapati
- 17 May 2022
- journal article
- research article
- Published by Elsevier in Applied Acoustics
- Vol. 195
Abstract
No abstract availableKeywords
This publication has 51 references indexed in Scilit:
- Fault diagnosis of ball bearings using machine learning methodsExpert Systems with Applications, 2011
- Combining bagging, boosting, rotation forest and random subspace methodsArtificial Intelligence Review, 2010
- Emerging applications of wavelets: A reviewPhysical Communication, 2010
- Reliability of wind turbine subassembliesIET Renewable Power Generation, 2009
- Gear crack level identification based on weighted K nearest neighbor classification algorithmMechanical Systems and Signal Processing, 2009
- A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniquesWear, 2005
- An integrated approach to fault diagnosis of machinery using wear debris and vibration analysisWear, 2003
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- An Introduction to Kernel and Nearest-Neighbor Nonparametric RegressionThe American Statistician, 1992
- Stacked generalizationNeural Networks, 1992