Active Thermographic Structural Feature Inspection of Wind-Turbine Rotor

Fey, Jens; Djahan, Cedric; Mpouma, Thierry Arnaud; Neh-Awah, Julienne; Handmann, Uwe

doi:10.1109/fendt.2017.8584610

Cited by 6 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Test Resultsmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…On the other hand, the smart city concept may encounter some obstacles about the security and privacy issues, which drives much research to work on like, recognizing people' faces [10,11] to access restricted areas [8][9][10], improving traffic flows by partly autonomous drones and vehicles [12] [13], traffic management and smart tracking, assistance systems [14,15], predictive maintenance [16,17], and last but not least, smart waste management [18].…”

Section: Circular Smart Citiesmentioning

confidence: 99%

Transfer learning-based method for automated e-waste recycling in smart cities

Baker¹,

Szabo-Müller²,

Handmann³

2018

EAI Endorsed Transactions on Smart Cities

Self Cite

View full text Add to dashboard Cite

INTRODUCTION: Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, instead of traditional methods. The overlap of Artificial Intelligence and Circular Economy can flourish many services in the environmental technology domain, in particular smart e-waste recycling, resulting in enabling circular smart cities. OBJECTIVES: We analyse the growing need for automated e-waste recycling as an essential requirement to cope with the fast-growing e-waste stream and we shed the light on the impact of Artificial Intelligence in supporting the recycling process through smart classification of devices, where the smartphone is our case study. METHODS: Our study applies transfer learning as a special technique of Artificial Intelligence by fine-tuning the output layers of AlexNet as a pre-trained model and perform the implementation on a small-size dataset that contains 12 classes from 6 smartphone brands. RESULTS: We evaluate the performance of our model by tuning the learning rate, choosing the best optimizer, and augmenting the original dataset to avoid overfitting. We found that the optimizer of Stochastic Gradient Descent with Momentum and 3 −4 as a learning rate brings almost 98% model accuracy with generalization. CONCLUSION: Our study supports automated e-waste recycling in decreasing the error-rate of e-waste sorting and investigates the advantages of applying transfer learning as the best scenario to overcome the rising challenges.

show abstract