Wind turbines lubricant gearbox degradation detection by means of a lossy mode resonance based optical fiber refractometer

Sanchez, Pedro; Mendizabal, D.; González, K; Zamarreño, Carlos R.; Hernáez, Miguel; Matı́as, Ignacio R.; Arregui, Francisco J.

doi:10.1007/s00542-015-2791-1

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…The layer morphology is compact but also rod-like (see Figure 25 ). A sensor prepared this manner was successfully evaluated for monitoring the aging of gear box oil as a function of its refractive index change [ 116 ].…”

Section: Nano and Microstructured Materials For Sensingmentioning

confidence: 99%

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Micro and Nanostructured Materials for the Development of Optical Fibre Sensors

Elosúa

Arregui

Villar

et al. 2017

Sensors

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The measurement of chemical and biomedical parameters can take advantage of the features exclusively offered by optical fibre: passive nature, electromagnetic immunity and chemical stability are some of the most relevant ones. The small dimensions of the fibre generally require that the sensing material be loaded into a supporting matrix whose morphology is adjusted at a nanometric scale. Thanks to the advances in nanotechnology new deposition methods have been developed: they allow reagents from different chemical nature to be embedded into films with a thickness always below a few microns that also show a relevant aspect ratio to ensure a high transduction interface. This review reveals some of the main techniques that are currently been employed to develop this kind of sensors, describing in detail both the resulting supporting matrices as well as the sensing materials used. The main objective is to offer a general view of the state of the art to expose the main challenges and chances that this technology is facing currently.

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Section: Nano and Microstructured Materials For Sensingmentioning

confidence: 99%

“…The resulting coating is homogeneous and it is formed by compact nanorods. Reprinted [ 116 ] from with permission from Springer.…”

Section: Figurementioning

confidence: 99%

Micro and Nanostructured Materials for the Development of Optical Fibre Sensors

Elosúa

Arregui

Villar

et al. 2017

Sensors

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“…The picture also shows that maintenance works of wind turbines gearbox need 18.38 days to finish it which is in the second for per failure due to their size and robust link to other components making it harder to access, repair, or even replace [6]. Such long maintenance time and according another research [7][8][9] can fully illustrates above opinion that it is hard to repair the faults of wind turbine gearbox in the wind field. Moreover, the expected operation life of gearbox is usually publicized as 20 years from merchant.…”

Section: Introductionmentioning

confidence: 98%

A review on wind turbines gearbox fault diagnosis methods

Gu¹,

Liu²,

Gao³

et al. 2021

J. vibroeng.

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As an renewable and clean energy of the world, wind energy has gained more and more attention and its fault diagnosis becomes more and more important. The gearbox, as the kernel component of the wind turbine system, it's robust conditions have a great influence on the whole wind turbines system. Wind turbine gearbox has complex structure, which is usually composed of solar planetary gearbox and cylindrical gearbox. In the process of operation, various kinds of faults easily occur, resulting in serious losses. Once the wind turbine gearbox is not functioning as smoothly as it could be, it may result in large economic losses for the company and owner. At the same time, the failure rate of wind turbine gearbox has always been high because of complicated mechanic structure and special motion. Therefore, the tasks of reducing the downtime and increasing the productivity of wind turbine gearbox are urgent. This paper reviewed some research results of faults diagnosis on wind turbines gearbox, such as time-frequency analysis method, vibration based methods, nondestructive testing methods, etc. Meanwhile, this paper finds out some key problems and the channel of the resolution of the issue in order to supply some information for the further research of wind turbines gearbox.

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“…Vibration analysis and oil monitoring have become two commonly used techniques for wind turbine condition monitoring [3][4][5][6]. However, both techniques are sophisticated and expensive in their practical application, since additional investments, including installing extra sensors and data acquisition devices, are required.…”

Section: Introductionmentioning

confidence: 99%

Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

Wang¹,

Wang²,

Jiang³

et al. 2019

Energies

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Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.

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Wind turbines lubricant gearbox degradation detection by means of a lossy mode resonance based optical fiber refractometer

Cited by 13 publications

References 21 publications

Micro and Nanostructured Materials for the Development of Optical Fibre Sensors

Micro and Nanostructured Materials for the Development of Optical Fibre Sensors

A review on wind turbines gearbox fault diagnosis methods

Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

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