Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

Pavlack, Bruna; Paixão, Jessé; Silva, Samuel da; Cunha, Americo; Cava, David García

doi:10.1177/14759217211007956

Cited by 12 publications

(6 citation statements)

References 39 publications

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“…Pavlack et al use the Jennic JN5139 wireless sensor network module to target specific objects (single-phase induction motor). A new industrial wireless sensor network (IWSN) for mechanical condition monitoring and fault diagnosis is proposed, by collecting the current signal and acceleration signal output by the rotor, then the signal feature extraction and neural network are used for fault classification, and finally, the fault fusion is performed at the routing node to obtain the operating state of the mechanical equipment [4]. Na et al developed an aircraft condition monitoring system based on the ZigBee wireless sensor network, designed a monitoring node composed of a CC2431 wireless SOC chip and a convergence node composed of CC2431 and C8051F340 as the core, and selected a 2 GB TF card as the data storage system.…”

Section: Literature Reviewmentioning

confidence: 99%

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Niu¹

2022

Security and Communication Networks

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In order to apply wireless sensor networks to mechanical vibration monitoring, the author proposes a wireless network topology with multiple data collection points for mechanical vibration monitoring. This structure reduces the transmission load of the data collection point, increases the data transmission rate of the network, balances the energy dissipation in the network, and utilizes the general wireless sensor network hardware platform. The network transmission protocol and related auxiliary mechanisms are designed and implemented, and a wireless vibration monitoring test platform is constructed. The transmission performance of the network structure with multiple data collection points is evaluated through the actual test. The experimental results show that by using the wireless sensor network topology with multiple data collection points, it can meet the requirements of continuous transmission of vibration data obtained by 1 kHz sampling. Conclusion. The system performance of the wireless sensor network based on this network structure has been improved under the condition of general hardware, and the network structure of multiple data collection points shows good performance in the process of high-speed data transmission.

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Section: Literature Reviewmentioning

confidence: 99%

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Niu¹

2022

Security and Communication Networks

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“…Hsu et al 11 propose a damage detection approach for rotating WT blades using the local flexibility method based on the dynamic macro-strain signals measured by long-gauge fiber brag grating-based sensors. Pavlack et al 12 proposed the method to quantify the severity of the damage. It is described by a specific type of debonding in a WT blade as a function of a damage index.…”

Section: Introductionmentioning

confidence: 99%

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Jiang

Xiang

2023

Structural Health Monitoring

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Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.

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“…Debonding detection and localization on a numerical model of a rear spoiler of a civilian aircraft was performed in [34]; however, the proposed approach lacks experimental validation, different loading conditions and it is only based on the deviation from the zero-baseline strain profile. A datadriven metamodel using polynomial Chaos and Kriging was developed to quantify the debonding area in a large wind turbine blade instrumented with accelerometers, and tested in a laboratory environment [35].…”

Section: Introductionmentioning

confidence: 99%

Debonding quantification in adhesive bonded joints by the inverse finite element method

Poloni

Oboe²,

Sbarufatti³

et al. 2023

Smart Mater. Struct.

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In the past two decades, the aerospace industry has massively shifted from Aluminum-made components to Composite Materials such as Carbon Fibre Reinforced Polymers (CFRP), striving for more fuel efficient and lighter aircrafts. Consequently, traditional joints have been replaced by adhesive bonded interfaces, which are also the most common choice to repair damaged components. Although adhesive bonding is the most efficient choice for permanent connections, it is not free of disadvantages: one of the most common failure modes, the debonding of the two laps, is very problematic to detect and predict in practice. Therefore, frequent inspections must be performed to ensure structural safety, increasing maintenance costs, and lessening the availability of the platforms. The development of innovative sensing technologies has allowed for a close monitoring of structural interfaces, and several Structural Health Monitoring techniques have been proposed to monitor adhesive bonded connections. Sensitivity and correlation between measurements and debonding entity has been demonstrated in the literature: nevertheless, hardly any technique has been proposed and quantitively evaluated to estimate the debonding entity independently of the applied loads, such as misalignment-induced torsion, which is a major confounding influence in the traditional backface strain gauge technique. This paper proposes the inverse Finite Element Method (iFEM) as a load and material independent approach to infer the debonding entity from strain measurements in adhesive-bonded joints. Two approaches to estimate the debonding entity with the iFEM are compared on Cracked Leap Shear specimens representative of CFRP repair patches: one is based on anomaly indexes, the other on performing a model selection with multiple iFEM models including different damages. The latter demonstrates satisfactory performances; thus, it is considered a significant scientific advancement in this field.

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Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

Cited by 12 publications

References 39 publications

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Debonding quantification in adhesive bonded joints by the inverse finite element method

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