Fusion of a FBG-based health monitoring system for wind turbines with a fiber-optic lightning detection system

Krämer, S.; Wiesent, Benjamin R.; Müller, Mathias S.; León, Fernando Puente; Hernández, Y Méndez

doi:10.1117/12.783602

Cited by 5 publications

(2 citation statements)

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“…Li et al [13] used FBG sensors to study material strain data from layered fabricated structures and to obtain information on the performance and structural integrity of wind turbine blades in harsh environments. Krämer et al [14] used an FBG network embedded in the composite structure of a wind turbine blade to measure blade strain data. However, FBG sensors also have their own limitations in wind turbine blade damage detection, such as uncertainty of the damage to the material structure caused by the embedded installation method, the impracticality of a large blade installation, and the difficulty associated with direct damage detection.…”

Section: Strain Detectionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

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Section: Strain Detectionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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“…Optical fiber grating sensor has many prominent benefits [1], and with the wide employment of fiber grating sensors in engineering, it becomes more and more urgent to process the signal of grating sensing effectively [2]. Data fusion can solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Pretreatment of Data Fusion in FBG Temperature Sensing Network

Yuan

Lei

Tong

et al. 2011

AMR

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After analyzing noise and disturbance in FBG temperature sensing network, three different kinds of statistical methods are processed in experiment as pretreatment to deal with inaccuracy of data fusion results caused by outliers. After replacing outliers with statistical estimated value of three methods respectively, weight minimum mean square error (WMMSE) algorithm is operated to fuse majorized data. Pretreatment makes full use of sensors’ data, meanwhile, accuracy and robustness of measurement system are enhanced. Variances of the fusion results indicate that pretreatment can improve fusion results significantly. Fusion variance without pretreatment is 2.9762, larger than mean value method’ variance 1.9525, median method’s 1.3341 and trimmed mean method’s 1.6016.

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Structural Health Monitoring of Wind Turbine Blades

Zhou

2014

Advances in Industrial Control

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Fusion of a FBG-based health monitoring system for wind turbines with a fiber-optic lightning detection system

Cited by 5 publications

References 0 publications

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Pretreatment of Data Fusion in FBG Temperature Sensing Network

Structural Health Monitoring of Wind Turbine Blades

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