Wind turbines fault diagnosis using ensemble classifiers

Authors:
Pedro Santos;Luisa F. Villa;Aníbal Reñones;Andrés Bustillo;Jesús Maudes
Affiliations:
Department of Civil Engineering, University of Bugos, Burgos, Spain;CARTIF Foundation, Parque Tecnológico de Boecillo, Boecillo, Valladolid, Spain;CARTIF Foundation, Parque Tecnológico de Boecillo, Boecillo, Valladolid, Spain;Department of Civil Engineering, University of Bugos, Burgos, Spain;Department of Civil Engineering, University of Bugos, Burgos, Spain
Venue:
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Year:
2012

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Abstract

Fault diagnosis in machines that work under a wide range of speeds and loads is currently an active area of research. Wind turbines are one of the most recent examples of these machines in industry. Conventional vibration analysis applied to machines throughout their operation is of limited utility when the speed variation is too high. This work proposes an alternative methodology for fault diagnosis in machines: the combination of angular resampling techniques for vibration signal processing and the use of data mining techniques for the classification of the operational state of wind turbines. The methodology has been validated over a test-bed with a large variation of speeds and loads which simulates, on a smaller scale, the real conditions of wind turbines. Over this test-bed two of the most common typologies of faults in wind turbines have been generated: imbalance and misalignment. Several data mining techniques have been used to analyze the dataset obtained by order analysis, having previously processed signals with angular resampling technique. Specifically, the methods used are ensemble classifiers built with Bagging, Adaboost, Geneneral Boosting Projection and Rotation Forest; the best results having been achieved with Adaboost using C4.5 decision trees as base classifiers.