A significant number of wind turbines will reach the end of their planned service life in the near future. A decision on lifetime extension is complex and experiences to date are limited. This review presents the current state-of-the-art for lifetime extension of onshore wind turbines in Germany, Spain, Denmark, and the UK. Information was gathered through a literature review and 24 guideline-based interviews with key market players. Technical, economic and legal aspects are discussed. Results indicate that end-of-life solutions will develop a significant market over the next five years. The application of updated load simulation and inspections for technical lifetime extension assessment differs between countries. A major concern is the uncertainty about future electricity spot market prices, which determine if lifetime extension is economically feasible.

Lifetime extension of onshore wind turbines: A review covering Germany, Spain, Denmark, and the UK

Damage detection techniques for wind turbine blades: A review

Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: Degradation models and strategies

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning. Before the rise of deep learning, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in deep learning, the extraction of features and classification is entirely automated. The advent of these techniques in many areas of medicine such as diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of the types of deep learning methods exploited to diagnose epileptic seizures from various modalities has been studied. Additionally, hardware implementation and cloud-based works are discussed as they are most suited for applied medicine.

Epileptic seizure detection using deep learning techniques: A Review

FPGA-based real-time epileptic seizure classification using Artificial Neural Network

Consideration of lifetime and fatigue load in wind turbine control

In this contribution, Structural Health Monitoring (SHM) systems applied to wind turbines (WTs) are considered. Challenges resulting from contradictions between requirements related to efficient operation with respect to energy production costs and those related to lifetime and maintenance are discussed. Especially pronounced in larger WT systems, structural loads contribute to lifetime shortening due to damage accumulation and damage-caused effects influencing subsystems of the wind turbine. Continuous monitoring of the WT system concerning State-of-Health is necessitated to provide information about the condition of the system guaranteeing reliable and efficient operation, as well as efficient energy extraction. In recent years, structural health monitoring of WT systems is significantly improved through automated on-line fault detection and health or condition monitoring (CM) system integration. In this contribution the focus is given to hardware components (mainly sensor technologies) and methods used for change evaluation, damage detection, and damage accumulation estimation. Accordingly, this contribution comprises recent knowledge about methods and approaches of handling structural loads with emphasis on offshore wind turbine systems and applied sensing technologies (especially with respect to wind turbine blades, gearboxes, and bearings) and partly hardware. Moreover, a brief sketch of an advanced concept is developed concerning structural load examination affected by operating conditions. Key idea of the introduced approach is to use the operating conditions to control and especially to extend system׳s lifetime. The review presents an actual state-of-the-art and overview related to the use and application of SHM-related technologies and methods. Especially in combination with the briefly introduced lifetime extension concept, the contribution gives comprehensive and detailed overview in combination with an outlook to upcoming technological options.

Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained results

Remaining lifetime modeling using State-of-Health estimation

Advances in composite technology led to the substitution of conventional, metallic construction material by composites. However, the more widespread application of composites is currently restricte...

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Nejra Beganovic

Papers

FPGA-based real-time epileptic seizure classification using Artificial Neural Network

Consideration of lifetime and fatigue load in wind turbine control

Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained results

Remaining lifetime modeling using State-of-Health estimation

Investigation of damage detectability in composites using frequency-based classification of acoustic emission measurements: