Analysis of Vibration Signals Based on Machine Learning for Crack Detection in a Low-Power Wind Turbine

Author:

Rangel-Rodriguez Angel H.1,Granados-Lieberman David2ORCID,Amezquita-Sanchez Juan P.1ORCID,Bueno-Lopez Maximiliano3ORCID,Valtierra-Rodriguez Martin1ORCID

Affiliation:

1. ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Mexico

2. ENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energía Eléctrica, Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México/ITS de Irapuato, Carretera Irapuato-Silao km 12.5, Colonia El Copal, Irapuato 36821, Mexico

3. Departamento de Electrónica, Instrumentación y Control, Universidad del Cauca, Popayán 190002, Colombia

Abstract

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%.

Publisher

MDPI AG

Subject

General Physics and Astronomy

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