Maintenance management of wind turbines structures via MFCs and wavelet transforms

González-Carrato, Raúl Ruiz de la Hermosa; Márquez, Fausto Pedro García; Dimlaye, Vichaar

doi:10.1016/j.rser.2015.04.007

Cited by 47 publications

(26 citation statements)

References 75 publications

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“…The novel signal processing is based on system identification techniques in discrete time to estimate potential faults. The wavelet and Hilbert transforms are employed to work in conjunction with an automatic peak detection algorithm [5]. The algorithm detects which peaks correspond to echoes from the edges, and which correspond to potential defects.…”

Section: The Objective Of This Paper Is To Demonstrate a Novel Signalmentioning

confidence: 99%

A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

Muñoz¹,

Márquez²,

Jiménez³

et al. 2017

EiN

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Science and Technology article citation info: IntroductionNon-destructive testing (NDT) for fault detection in structures has received more attention in recent years. Significant advances in instrumentation technology and digital signal processing have been made [16,17,19,25,30]. Signal processing methods together with structural health monitoring (SHM) permit the identification and diagnosis of faults and their location based on changes in static and

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Section: The Objective Of This Paper Is To Demonstrate a Novel Signalmentioning

confidence: 99%

A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

Muñoz¹,

Márquez²,

Jiménez³

et al. 2017

EiN

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“…The maintenance management proposed in this paper aims to maximise the RAMS of the offshore wind farms optimising the resources such as human or material, conditioned to exogenous variables, e.g., weather conditions [74]. This approach is based on the probability of failure of each WT.…”

Section: Maintenance Management Approachmentioning

confidence: 99%

Optimal Maintenance Management of Offshore Wind Farms

2016

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Abstract:Nowadays offshore wind energy is the renewable energy source with the highest growth. Offshore wind farms are composed of large and complex wind turbines, requiring a high level of reliability, availability, maintainability and safety (RAMS). Firms are employing robust remote condition monitoring systems in order to improve RAMS, considering the difficulty to access the wind farm. The main objective of this research work is to optimise the maintenance management of wind farms through the fault probability of each wind turbine. The probability has been calculated by Fault Tree Analysis (FTA) employing the Binary Decision Diagram (BDD) in order to reduce the computational cost. The fault tree presented in this paper has been designed and validated based on qualitative data from the literature and expert from important European collaborative research projects. The basic events of the fault tree have been prioritized employing the criticality method in order to use resources efficiently. Exogenous variables, e.g., weather conditions, have been also considered in this research work. The results provided by the dynamic probability of failure and the importance measures have been employed to develop a scheduled maintenance that contributes to improve the decision making and, consequently, to reduce the maintenance costs.

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“…SHM can be remotely managed, reducing the costs of manual inspections and the time between the fault occurrences, and this has been noted [11,12]. This will lead to an increase in the productivity, reducing the potential downtimes for the wind farms and increasing the RAMS of the wind turbine [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines

Muñoz

Márquez

2016

Energies

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The renewable energy industry is undergoing continuous improvement and development worldwide, wind energy being one of the most relevant renewable energies. This industry requires high levels of reliability, availability, maintainability and safety (RAMS) for wind turbines. The blades are critical components in wind turbines. The objective of this research work is focused on the fault detection and diagnosis (FDD) of the wind turbine blades. The FDD approach is composed of a robust condition monitoring system (CMS) and a novel signal processing method. CMS collects and analyses the data from different non-destructive tests based on acoustic emission. The acoustic emission signals are collected applying macro-fiber composite (MFC) sensors to detect and locate cracks on the surface of the blades. Three MFC sensors are set in a section of a wind turbine blade. The acoustic emission signals are generated by breaking a pencil lead in the blade surface. This method is used to simulate the acoustic emission due to a breakdown of the composite fibers. The breakdown generates a set of mechanical waves that are collected by the MFC sensors. A graphical method is employed to obtain a system of non-linear equations that will be used for locating the emission source. This work demonstrates that a fiber breakage in the wind turbine blade can be detected and located by using only three low cost sensors. It allows the detection of potential failures at an early stages, and it can also reduce corrective maintenance tasks and downtimes and increase the RAMS of the wind turbine.

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Maintenance management of wind turbines structures via MFCs and wavelet transforms

Cited by 47 publications

References 75 publications

A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

Optimal Maintenance Management of Offshore Wind Farms

A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines

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