Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

Jiménez, Alfredo; Muñoz, Carlos Quiterio Gómez; Márquez, Fausto Pedro García

doi:10.1016/j.ress.2018.02.013

Cited by 96 publications

(42 citation statements)

References 55 publications

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“…has special significance in the operation of the wind turbine [16], similar to other elements on the WTB surface, e.g. dirt and mud [17,18]. The wind turbine is stopped when a certain mass of ice is reached [19].…”

Section: Ncamentioning

confidence: 99%

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

et al. 2019

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The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results.

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Section: Ncamentioning

confidence: 99%

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

et al. 2019

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“…Wind farms are located in remote areas under the severe weather conditions and, consequently, each wind turbine presents problems related to ice and snow deposition on the WTBs, breakage of WTBs by impact of objects, etc. [4,5]. The wind turbine rotor, electrical devices, plant control system, hydraulic and sensors have more than 50% of total failures [6,7].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing

Márquez

Muñoz

2020

Energies

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Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind turbine blades, modifying their behaviour due, for example, to the turbulence. A new method is presented in this paper based on cross-correlations to determine the presence of delamination in the blades. The experiments were conducted in two real wind turbine blades to analyse the fault and non-fault blades using ultrasonic guided waves. Finally, the energy analysis of the signal based on wavelet transforms allowed to determine energies abrupt changes in the correlation of the signals and to locate the faults.

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“…We evaluate the proposed method for the detection and classification of structural changes using experimental data from an aluminum plate instrumented with piezoelectric transducers (PZTs) attached to its surface. Because guided wave propagation‐based SHM strategies have proven their ability to adequately identify defects in structures, in our study, we have also considered the paradigm of guided waves. In this paradigm, the structure is excited by a signal, and the response is measured to create a baseline pattern.…”

Section: Introductionmentioning

confidence: 99%

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

Agis

Burgos

Pozo

2020

Struct Control Health Monit

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Summary This paper describes a structural health monitoring strategy to detect and classify structural changes in structures that can be equipped with sensors. The proposed approach is based on the t‐distributed stochastic neighbor embedding ( t‐SNE), a nonlinear technique that can represent the local structure of high‐dimensional data collected from multiple sensors in a plane or spatial representation. We propose the following basic steps for the detection and classification. First, the raw data are preprocessed: We scale the data using the mean‐centered group scaling and apply principal component analysis to reduce the dimensionality of the scaled data. Second, t‐SNE is applied to represent the scaled and reduced data as points in a plane, defining a cluster for each structural state. Finally, the current structure to be diagnosed is associated with a cluster (or structural state) using three different strategies: (a) the smallest point‐centroid distance; (b) the majority voting; and (c) the sum of the inverse distances. The combination of t‐SNE with our preprocessing and the three proposed classification strategies significantly improves the quality of the clusters that represent different structural states. We evaluate the performance of our method using experimental data from an aluminum plate instrumented with piezoelectric transducers. Results are presented in the time domain, and they reveal the high classification accuracy and strong performance of this method, with a percentage of correct decisions close to 100% in several scenarios.

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Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

Abstract: Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliability Engineering & System Safety.

Cited by 96 publications

References 55 publications

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

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