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Henrique Dias Machado de Azevedo

Bio: Henrique Dias Machado de Azevedo is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Condition monitoring & Turbine. The author has an hindex of 2, co-authored 2 publications receiving 190 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors provide a review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis.
Abstract: Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.

248 citations

Journal ArticleDOI
TL;DR: In this paper, a vibration-based condition monitoring methodology was applied using signal processing techniques such as Fourier transform and envelope analysis with Hilbert transform to detect and prevent unnecessary maintenance breakdowns and costs.
Abstract: Wind turbines are growing rapidly in size and diameter. Nowadays, most wind turbines being installed are around 100 m in height and 80–120 m in diameter. Another important characteristic of wind farms is that they are usually far from urban centers. These peculiarities play an important role when analyzing the operation and maintenance costs and its impact in the wind farm project. In remote centers, it becomes crucial to predict and prevent unnecessary maintenance breakdowns and costs. An efficient solution to prevent faults on wind turbines is through condition monitoring. Faults could be prevented by analyzing data from sensors placed around the wind turbines to measure mainly oil quality, temperature, and vibration. In this paper, accelerometers were placed on the main components of a real wind turbine and a vibration-based condition monitoring methodology was applied using signal processing techniques such as Fourier transform, and envelope analysis with Hilbert transform. A bearing fault was discovered and the vibration characteristics were analyzed before and after the bearing replacement.

8 citations


Cited by
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TL;DR: This paper reviews the recent literature on machine learning models that have been used for condition monitoring in wind turbines and shows that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression.

482 citations

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TL;DR: A systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection is provided.

312 citations

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TL;DR: This paper aims at systematically and comprehensively summarizing current large-scale wind turbine bearing failure modes and condition monitoring and fault diagnosis achievements, followed by a brief summary of future research directions for wind turbine Bearing fault diagnosis.

249 citations

Journal ArticleDOI
TL;DR: A review on different methods and techniques for gearbox condition monitoring in wind turbines aiming to increase lifetime expectancy of components while reducing operation and maintenance cost is gathered.

226 citations

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TL;DR: In this paper, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement in a two-stage gearbox as well as train bearings.

219 citations