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In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the “WinJi Gearbox Fault Detection Challenge”, based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for rep, Wind Energy

Die zunehmende Abkehr vom ursprünglichen EEG-Vergütungssystem mit einer festen Einspeisevergütung hin zu einer mehr und mehr marktorientierten Ausrichtung führt zu der Frage, ob die Umstrukturierung des EEG am Ende zu einer neuen Phase der Energiewende führt, der Neo-EEG-Phase. Im vorliegenden Artikel werden die Veränderungen und Entwicklungsphasen des EEG mit besonderem Blick auf die Akteure des Stromsystems analysiert. Im Kontext der Energiewende können die zu beobachtenden und teils deutlich einschneidenden Veränderungen für alle Akteure des Systems durchaus als "Akteurswende" verstanden werden.

We discuss numerical modeling attacks on several proposed strong physical unclonable functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. If successful, this algorithm can subsequently impersonate the Strong PUF, and can be cloned and distributed arbitrarily. It breaks the security of any applications that rest on the Strong PUF's unpredictability and physical unclonability. Our method is less relevant for other PUF types such as Weak PUFs. The Strong PUFs that we could attack successfully include standard Arbiter PUFs of essentially arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs up to certain sizes and complexities. We also investigate the hardness of certain Ring Oscillator PUF architectures in typical Strong PUF applications. Our attacks are based upon various machine learning techniques, including a specially tailored variant of logistic regression and evolution strategies. Our results are mostly obtained on CRPs from numerical simulations that use established digital models of the respective PUFs. For a subset of the considered PUFs-namely standard Arbiter PUFs and XOR Arbiter PUFs-we also lead proofs of concept on silicon data from both FPGAs and ASICs. Over four million silicon CRPs are used in this process. The performance on silicon CRPs is very close to simulated CRPs, confirming a conjecture from earlier versions of this work. Our findings lead to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike., National Science Foundation (U.S.) (Grant CNS 0923313), National Science Foundation (U.S.) (Grant CNS 0964641)

Reinforcement Learning is the most commonly used class of learning algorithms which lets robots or other systems autonomously learn their behaviour. Learning is enabled solely through interaction with the environment. Today’s learning systems are often confronted with high dimensional and continuous problems. To solve those, so-called Policy Gradient methods are used more and more often. The PGPE algorithm developed in this thesis, a new type of Policy Gradient algorithm, allows model-free learning in complex, continuous, partially observable and high dimensional environments. We show that tasks like grasping of glasses and plates with an human-like arm can be learned with this method without prior knowledge, solely with pure model-free reinforcement learning in a simulation environment. Also, the balancing of a humanoid robot perturbed by external forces, as well as dynamic walking behaviour of a mass-spring system could be learned. In all experiments, PGPE learned the given tasks more efficiently than well-established methods. In addition, the use of PGPE is not restricted to robotics. Among several investigated methods, it was the most successful in cracking non-differentiable physical cryptography systems. PGPE is suitable for training multidimensional recurrent neural networks to play Go, or for fine-tuning deep neural nets for computer vision. In the scope of this thesis, the principles used, the advantages and disadvantages as well as the differences with regard to well-established methods are derived and analysed in detail., Reinforcement Learning (Bestärkendes Lernen) ist die am häufigsten verwendete Klasse von Lernalgorithmen, um Robotern oder anderen Systemen das selbständige Erlernen ihres Verhalten zu ermöglichen. Lernen geschieht hierbei allein durch Interaktion des Systems mit seiner Umwelt. Heutige lernende Systeme haben es oft mit hochdimensionalen und kontinuierlichen Problemen zu tun. Hierfür kommen vermehrt die so genannten Policy Gradient Methoden zum Einsatz. Der in dieser Arbeit entwickelte PGPE-Algorithms, ein neuer Typ von Policy Gradients, ermöglicht modellfreies Lernen in komplexen, kontinuierlichen, nur teilweise beobachtbaren und hochdimensionalen Umgebungen. Wir zeigen, dass hiermit ohne Vorwissen, durch reines modellfreies bestärkendes Lernen in einer Simulationsumgebung, Aufgaben wie das Greifen von Gläsern und Tellern mit einem dem menschlichen Arm nachempfundenen Roboter erlernt werden. Auch das Balancieren eines humanoiden Roboters der von externen Kräften gestört wird, sowie das dynamische Laufen eines Masse-Feder Systems wurden erlernt. In allen Experimenten lernte PGPE die Aufgaben effizienter als etablierte Methoden. Der Einsatz von PGPE beschränkt sich dabei nicht auf die Robotik. Sie ist die erfolgreichste Methode unter den untersuchten um nicht differenzierbare physikalische Kryptographie Systeme zu brechen. Sie ist geeignet um multidimensionale rekurrente neuronale Netze zu trainieren, Go zu spielen oder um tiefe neuronale Netze für die Bildverarbeitung nachzutrainieren. Die Prinzipien, welche hierbei zur Anwendung kamen, die Vor- und Nachteile sowie die Unterschiede gegenüber den etablierten Methoden werden im Rahmen der Arbeit im Detail hergeleitet und analysiert.

Reinforcement Learning is the most commonly used class of learning algorithms which lets robots or other systems autonomously learn their behaviour. Learning is enabled solely through interaction with the environment. Today’s learning systems are often confronted with high dimensional and continuous problems. To solve those, so-called Policy Gradient methods are used more and more often. The PGPE algorithm developed in this thesis, a new type of Policy Gradient algorithm, allows model-free learning in complex, continuous, partially observable and high dimensional environments. We show that tasks like grasping of glasses and plates with an human-like arm can be learned with this method without prior knowledge, solely with pure model-free reinforcement learning in a simulation environment. Also, the balancing of a humanoid robot perturbed by external forces, as well as dynamic walking behaviour of a mass-spring system could be learned. In all experiments, PGPE learned the given tasks more efficiently than well-established methods. In addition, the use of PGPE is not restricted to robotics. Among several investigated methods, it was the most successful in cracking non-differentiable physical cryptography systems. PGPE is suitable for training multidimensional recurrent neural networks to play Go, or for fine-tuning deep neural nets for computer vision. In the scope of this thesis, the principles used, the advantages and disadvantages as well as the differences with regard to well-established methods are derived and analysed in detail., Reinforcement Learning (Bestärkendes Lernen) ist die am häufigsten verwendete Klasse von Lernalgorithmen, um Robotern oder anderen Systemen das selbständige Erlernen ihres Verhalten zu ermöglichen. Lernen geschieht hierbei allein durch Interaktion des Systems mit seiner Umwelt. Heutige lernende Systeme haben es oft mit hochdimensionalen und kontinuierlichen Problemen zu tun. Hierfür kommen vermehrt die so genannten Policy Gradient Methoden zum Einsatz. Der in dieser Arbeit entwickelte PGPE-Algorithms, ein neuer Typ von Policy Gradients, ermöglicht modellfreies Lernen in komplexen, kontinuierlichen, nur teilweise beobachtbaren und hochdimensionalen Umgebungen. Wir zeigen, dass hiermit ohne Vorwissen, durch reines modellfreies bestärkendes Lernen in einer Simulationsumgebung, Aufgaben wie das Greifen von Gläsern und Tellern mit einem dem menschlichen Arm nachempfundenen Roboter erlernt werden. Auch das Balancieren eines humanoiden Roboters der von externen Kräften gestört wird, sowie das dynamische Laufen eines Masse-Feder Systems wurden erlernt. In allen Experimenten lernte PGPE die Aufgaben effizienter als etablierte Methoden. Der Einsatz von PGPE beschränkt sich dabei nicht auf die Robotik. Sie ist die erfolgreichste Methode unter den untersuchten um nicht differenzierbare physikalische Kryptographie Systeme zu brechen. Sie ist geeignet um multidimensionale rekurrente neuronale Netze zu trainieren, Go zu spielen oder um tiefe neuronale Netze für die Bildverarbeitung nachzutrainieren. Die Prinzipien, welche hierbei zur Anwendung kamen, die Vor- und Nachteile sowie die Unterschiede gegenüber den etablierten Methoden werden im Rahmen der Arbeit im Detail hergeleitet und analysiert.

We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF. The PUFs we attacked successfully include standard Arbited PUFs and Ring Oscillator PUFs of arbitrary sizes, and XO Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike., Technische Universitat Munchen