An Effective Method to Inspect Adhesive Quality of Wind Turbine Blades Using Transmission Thermography

Li, Xiaoli; Sun, Jie; Tao, Ning; Feng, Lichun; Shen, Jingling; He, Yi; Zhang, Cunlin; Zhao, Yuejin

doi:10.1007/s10921-018-0473-5

Cited by 13 publications

(11 citation statements)

References 20 publications

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“…In order to have intelligent and proactive maintenance services for the farm, it is essential to develop monitoring in-frastructure based on Vision Inspection (VI) technologies. This can increase the lifetime of the WT farm and reduce the maintenance cost, provided that accurate faults and failures predictions are available via advanced Nondestructive tests approaches such as intelligent VI and thermal visions [6][7][8][9][10].…”

Section: Resultsmentioning

confidence: 99%

“…In case of failure and faulty operation, the grid will face interruptions in its service. Challenges and costly breakdowns such as mechanical deformations, surface defects, overheated components in rotor blades, nacelles, slip rings, yaw drives, bearings, gearbox, generators, and transformers are the ones which should be monitored to detect faults intelligently in a wind turbine farm [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Wind turbine tower detection using feature descriptors and deep learning

Abedini

Bahaghighat²,

Shoyan

2020

Facta Univ Electron Energ

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Wind Turbine Towers (WTTs) are the main structures of wind farms. They are costly devices that must be thoroughly inspected according to maintenance plans. Today, existence of machine vision techniques along with unmanned aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection of the structures. Our work is aimed towards developing a visionbased system to perform Nondestructive tests (NDTs) for wind turbines using UAVs. In order to navigate the flying machine toward the wind turbine tower and reliably land on it, the exact position of the wind turbine and its tower must be detected. We employ several strong computer vision approaches such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then, in order to increase the reliability of the system, we apply the ResNet, MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order to verify whether a detected object is indeed a turbine tower or not.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind turbine tower detection using feature descriptors and deep learning

Abedini

Bahaghighat²,

Shoyan

2020

Facta Univ Electron Energ

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“…The experimental results highlight the value of thermal imaging in detecting damages remotely when the blades are subject to operational loads. Instead of using an external heating source to excite thermal abnormalities, [22][23][24][25][26][27] we do not apply any thermal excitation to the blade in this study. The thermal footprints of damages are generated when the blade is under fatigue.…”

Section: Test Resultsmentioning

confidence: 99%

AQUADA: Automated quantification of damages in composite wind turbine blades for LCOE reduction

et al. 2020

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Reliability and cost are two important driving factors in the development of wind energy. Automation and digitalization of operation and maintenance (O&M) procedures help to increase turbine reliability and reduce the levelized cost of energy (LCOE). Here, we demonstrate a novel method, coined as AQUADA, which may change the current labor‐intensive and operation‐interfering blade inspection by using thermography and computer vision. We experimentally show that structural damages below the surfaces can be detected and quantified remotely when wind turbine blades are subject to fatigue loads. The data acquisition and analysis are automatically done in one single step, which may shift the current inspection paradigm through more automated O&M procedures. The cost analysis shows that the AQUADA method has a potential to at least half the total inspection cost and reduce the LCOE by 1%–2% when applied to a baseline land‐based wind farm consisting of twenty 2.45‐MW turbines. All data and source codes are published for researchers to reproduce our results and facilitate further development of AQUADA towards more mature industrial applications.

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“…For instance, Elkmann et al introduced a conceptual robotic system to move along wind turbine blades [78]. Different NDI techniques are already employed in several studies, such as terahertz inverse synthetic aperture radar, infrared thermography, and X-ray imaging [79][80][81].…”

Section: Inspection Of Wind Turbinesmentioning

confidence: 99%

Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies

Nooralishahi

Ibarra-Castanedo

Deane

et al. 2021

Drones

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Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

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An Effective Method to Inspect Adhesive Quality of Wind Turbine Blades Using Transmission Thermography

Cited by 13 publications

References 20 publications

Wind turbine tower detection using feature descriptors and deep learning

Wind turbine tower detection using feature descriptors and deep learning

AQUADA: Automated quantification of damages in composite wind turbine blades for LCOE reduction

Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies

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