REAL-TIME MONITORING OF WIND CONVERTERS BASED ON SOFTWARE AGENTS

Due to increasing numbers of wind energy converters, the accurate assessment of the lifespan of their structural parts and the entire converter system is becoming more and more paramount. Lifespan-oriented design, inspections and remedial maintenance are challenging because of their complex dynamic behavior. Wind energy converters are subjected to stochastic turbulent wind loading causing corresponding stochastic structural response and vibrations associated with an extreme number of stress cycles (up to 109 according to the rotation of the blades). Currently, wind energy converters are constructed for a service life of about 20 years. However, this estimation is more or less made by rule of thumb and not backed by profound scientific analyses or accurate simulations. By contrast, modern structural health monitoring systems allow an improved identification of deteriorations and, thereupon, to drastically advance the lifespan assessment of wind energy converters. In particular, monitoring systems based on artificial intelligence techniques represent a promising approach towards cost-efficient and reliable real-time monitoring. Therefore, an innovative real-time structural health monitoring concept based on software agents is introduced in this contribution. For a short time, this concept is also turned into a real-world monitoring system developed in a DFG joint research project in the authors’ institute at the Ruhr-University Bochum. In this paper, primarily the agent-based development, implementation and application of the monitoring system is addressed, focusing on the real-time monitoring tasks in the deserved detail

Automatisierte Planung von digitalen Hochgeschwindigkeitsnetzen

Der Ausbau von digitalen Hochgeschwindigkeitsnetzen ist gekennzeichnet durch neuartige Anforderungen an den Planungsprozess. Diese Anforderungen erfordern wiederum den Einsatz von neuartigen Paradigmen, die eine effiziente und zugleich genaue Planung von flächendeckenden Glasfasernetzen ermöglichen. Hierbei können wiederkehrende Planungsaufgaben durch eine gezielte computergestützte Automatisierung effizienter und genauer ausgeführt, als es mit bisherigen Planungskonzepten möglich ist. Dieses Arbeitspapier beschreibt die computergestützte Ausführung eines Planungsprozesses auf Basis von fünf grundlegenden, iterativen Planungsschritten und gibt Empfehlungen für eine effiziente und genaue Planung von Glasfasernetzen. Der hier vorgestellte Ansatz ermöglicht es Netzbetreibern und Investoren, den Ausbau beliebiger Siedlungs- und Gewerbegebiete auf der zuverlässigen Basis von belastbarem Faktenwissen wirtschaftlich zu priorisieren

DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

REAL-TIME MONITORING OF WIND CONVERTERS BASED ON SOFTWARE AGENTS

Due to increasing numbers of wind energy converters, the accurate assessment of the lifespan of their structural parts and the entire converter system is becoming more and more paramount. Lifespan-oriented design, inspections and remedial maintenance are challenging because of their complex dynamic behavior. Wind energy converters are subjected to stochastic turbulent wind loading causing corresponding stochastic structural response and vibrations associated with an extreme number of stress cycles (up to 109 according to the rotation of the blades). Currently, wind energy converters are constructed for a service life of about 20 years. However, this estimation is more or less made by rule of thumb and not backed by profound scientific analyses or accurate simulations. By contrast, modern structural health monitoring systems allow an improved identification of deteriorations and, thereupon, to drastically advance the lifespan assessment of wind energy converters. In particular, monitoring systems based on artificial intelligence techniques represent a promising approach towards cost-efficient and reliable real-time monitoring. Therefore, an innovative real-time structural health monitoring concept based on software agents is introduced in this contribution. For a short time, this concept is also turned into a real-world monitoring system developed in a DFG joint research project in the authors’ institute at the Ruhr-University Bochum. In this paper, primarily the agent-based development, implementation and application of the monitoring system is addressed, focusing on the real-time monitoring tasks in the deserved detail

A GENERIC FRAMEWORK SUPPORTING DISTRIBUTED COMPUTING IN ENGINEERING APPLICATIONS

Modern distributed engineering applications are based on complex systems consisting of various subsystems that are connected through the Internet. Communication and collaboration within an entire system requires reliable and efficient data exchange between the subsystems. Middleware developed within the web evolution during the past years provides reliable and efficient data exchange for web applications, which can be adopted for solving the data exchange problems in distributed engineering applications. This paper presents a generic approach for reliable and efficient data exchange between engineering devices using existing middleware known from web applications. Different existing middleware is examined with respect to the suitability in engineering applications. In this paper, a suitable middleware is shown and a prototype implementation simulating distributed wind farm control is presented and validated using several performance measurements

Kostengünstiger Breitbandausbau mittels automatisierter Integration von Installationsplänen und Installationskosten

Der Bedarf an leistungsfähigen Hochgeschwindigkeits-Glasfasernetzen ist in den letzten Jahren, insbesondere aufgrund der zunehmenden Nutzung von Internet-basierten Diensten, rapide gestiegen. Eine umfassende Planung von neu zu errichtenden Glasfasernetzen im Sinne eines kostengünstigen Breitbandausbaus ist jedoch häufig mit hohem Aufwand verbunden. Die Auswahl möglicher Kabelwege und die anschließende Kalkulation der Installationskosten werden heute in der Regel durch computerbasierte Verfahren unterstützt, wobei allerdings die Installationspläne und die Installationskosten getrennt voneinander dargestellt werden, was wiederum die computergestützte Planung sowie weitere Optimierungsansätze erschwert. Dieses Arbeitspapier beschreibt ein Konzept für ein modulares Softwaresystem zur computergestützten Planung, Kostenkalkulation und Visualisierung von Glasfasernetzen, das eine integrierte Darstellung von Installationsplänen und Installationskosten ermöglicht. Anstelle der herkömmlichen Darstellung von Installationsplänen, die in der Regel alle geplanten Kabelwege einfarbig auf einer Landkarte visualisiert, wird ein Farbschema zur Anzeige der Installationskosten in Installationsplänen eingesetzt. Das Konzept wird prototypisch implementiert und durch ein Anwendungsbeispiel, das die Planung eines Glasfasernetzes innerhalb eines Siedlungsgebietes behandelt, validiert. Die integrierte, farbige Darstellung der Installationskosten in Installationsplänen ermöglicht eine effiziente Identifikation der kostenintensiven Bauabschnitte und befördert kostenoptimierte Planungsansätze. Die intuitive Visualisierung vereinfacht somit die akkurate und kostenoptimierte Planung von Glasfasernetzen

IFC-BASED MONITORING INFORMATION MODELING FOR DATA MANAGEMENT IN STRUCTURAL HEALTH MONITORING

This conceptual paper discusses opportunities and challenges towards the digital representation of structural health monitoring systems using the Industry Foundation Classes (IFC) standard. State-of-the-art sensor nodes, collecting structural and environmental data from civil infrastructure systems, are capable of processing and analyzing the data sets directly on-board the nodes. Structural health monitoring (SHM) based on sensor nodes that possess so called “on-chip intelligence” is, in this study, referred to as “intelligent SHM”, and the infrastructure system being equipped with an intelligent SHM system is referred to as “intelligent infrastructure”. Although intelligent SHM will continue to grow, it is not possible, on a well-defined formalism, to digitally represent information about sensors, about the overall SHM system, and about the monitoring strategies being implemented (“monitoring-related information”). Based on a review of available SHM regulations and guidelines as well as existing sensor models and sensor modeling languages, this conceptual paper investigates how to digitally represent monitoring-related information in a semantic model. With the Industry Foundation Classes, there exists an open standard for the digital representation of building information; however, it is not possible to represent monitoring-related information using the IFC object model. This paper proposes a conceptual approach for extending the current IFC object model in order to include monitoring-related information. Taking civil infrastructure systems as an illustrative example, it becomes possible to adequately represent, process, and exchange monitoring-related information throughout the whole life cycle of civil infrastructure systems, which is referred to as monitoring information modeling (MIM). However, since this paper is conceptual, additional research efforts are required to further investigate, implement, and validate the proposed concepts and methods

ROBUST SCHEDULING IN CONSTRUCTION ENGINEERING

In construction engineering, a schedule’s input data, which is usually not exactly known in the planning phase, is considered deterministic when generating the schedule. As a result, construction schedules become unreliable and deadlines are often not met. While the optimization of construction schedules with respect to costs and makespan has been a matter of research in the past decades, the optimization of the robustness of construction schedules has received little attention. In this paper, the effects of uncertainties inherent to the input data of construction schedules are discussed. Possibilities are investigated to improve the reliability of construction schedules by considering alternative processes for certain tasks and by identifying the combination of processes generating the most robust schedule with respect to the makespan of a construction project

Implementierung eines webbasierten Talsperren-Monitoring-Systems

Die Bauwerksüberwachung gewinnt aus sicherheitstechnischen sowie aus wirtschaftlichen Gründen zunehmend an Bedeutung. Nicht nur die Bauwerkssicherheit kann durch leistungsfähige Monitoring-Systeme angemessen beurteilt, auch die Nutzungsdauer bestehender Bauwerke kann durch die gewonnenen Informationen deutlich verlängert werden. Das vorliegende Papier beschreibt die Entwicklung eines webbasierten Talsperren-Monitoring-Systems, das die automatisierte Erfassung von Daten vor Ort sowie die computergestützte Aufbereitung und Analyse der gesammelten Messdaten ermöglicht. Das Monitoring-System ist durch seinen modularen Aufbau nicht auf die Talsperren-Überwachung beschränkt, sondern kann ohne großen Aufwand an andere Überwachungsaufgaben angepasst werden. Das System besteht aus drei wesentlichen Modulen: (i) einer erweiterbaren Klassenbibliothek, die die Steuerung der im Bauwerk installierten Messelektronik ermöglicht, (ii) einem webbasierten Datenerfassungsmodul, das neben der automatischen Datenerfassung eine Fernsteuerung der Messelektronik erlaubt und Funktionen zur Verwaltung der Überwachungsaufgaben bereitstellt, sowie (iii) einem webbasierten Visualisierungs- und Auswertungsmodul zur Aufbereitung und Analyse der gesammelten Daten. Alle an der Überwachung beteiligten Mitarbeiter können mit einem üblichen Web-Browser über das Internet auf das entwickelte System zugreifen; ein Zugriff mittels Mobiltelefon ist alternativ möglich. Das implementierte Talsperren-Monitoring-System begleitet die beteiligten Fachleute von der Erfassung der Daten vor Ort bis hin zur Aufbereitung und Analyse der Messdaten an zentraler Stelle: Die Mitarbeiter werden durch einen einfachen Zugriff auf die installierte Messelektronik, automatisierte Messungen und umfangreiche Analysefunktionalitäten bei ihren spezifischen Aufgaben unterstützt. Der bisherige manuelle Arbeitsaufwand für Datenerfassung, -transfer und Analyse wird somit deutlich reduziert

DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems