Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Beurteilung der Resttragfähigkeit von Bauwerken mit Hilfe der Fuzzy-Logik und Entscheidungstheorie

Whereas the design of new structures is almost completely regulated by codes, there are no objective ways for the evaluation of existing facilities. Experts often are not familiar with the new tasks in system identification and try to retrieve at least some information from available documents. They therefore make compromises which, for many stakeholders, are not satisfying. Consequently, this publication presents a more objective and more realistic method for condition assessment. Necessary basics for this task are fracture mechanics combined with computational analysis, methods and techniques for geometry recording and material investigation, ductility and energy dissipation, risk analysis and uncertainty consideration. Present tools for evaluation perform research on how to analytically conceptualize a structure directly from given loads and measured response. Since defects are not necessarily visible or in a direct way detectable, several damage indices are combined and integrated in a model of the real system. Fuzzy-sets are ideally suited to illustrate parametric/data uncertainty and system- or model uncertainty. Trapezoidal membership functions may very well represent the condition state of structural components as function of damage extent or performance. Tthe residual load-bearing capacity can be determined by successively performing analyses in three steps. The "Screening assessment" shall eliminate a large majority of structures from detailed consideration and advise on immediate precautions to save lives and high economic values. Here, the defects have to be explicitly defined and located. If this is impossible, an "approximate evaluation" should follow describing system geometry, material properties and failure modes in detail. Here, a fault-tree helps investigate defaults in a systematic way avoiding random search or negligence of important features or damage indices. In order to inform about the structural system it is deemed essential not only due to its conceptual clarity, but also due to its applicational simplicity. It therefore represents an important prerequisite in condition assessment though special circumstances might require "fur-ther investigations" to consider the actual material parameters and unaccounted reserves due to spatial or other secondary contributions. Here, uncertainties with respect to geometry, material, loading or modeling should in no case be neglected, but explicitly quantified. Postulating a limited set of expected failure modes is not always sufficient, since detectable signature changes are seldom directly attributable and every defect might -together with other unforeseen situations- become decisive. So, a determination of all possible scenarios to consider every imaginable influence would be required. Risk is produced by a combination of various and ill-defined failure modes. Due to the interaction of many variables there is no simple and reliable way to predict which failure mode is dominant. Risk evaluation therefore comprises the estimation of the prognostic factor with respect to undesir-able events, component importance and the expected damage extent.Während die Bemessung von Tragwerken im allgemeinen durch Vorschriften geregelt ist, gibt es für die Zustandsbewertung bestehender Bauwerken noch keine objektiven Richtlinien. Viele Experten sind mit der neuen Problematik (Systemidentifikation anhand von Belastung und daraus entstehender Strukturantwort) noch nicht vertraut und begnügen sich daher mit Kompromißlösungen. Für viele Bauherren ist dies unbefriedigend, weshalb hier eine objektivere und wirklichkeitsnähere Zustandsbewertung vorgestellt wird. Wichtig hierfür sind theoretische Grundlagen der Schadensanalyse, Methoden und Techniken zur Geometrie- und Materialerkundung, Duktilität und Energieabsorption, Risikoanalyse und Beschreibung von Unsicherheiten. Da nicht alle Schäden offensichtlich sind, kombiniert man zur Zeit mehrere Zustandsindikatoren, bereitet die registrierten Daten gezielt auf, und integriert sie vor einer endgültigen Bewertung in ein validiertes Modell. Werden deterministische Nachweismethoden mit probabilstischen kombiniert, lassen sich nur zufällige Fehler problemlos minimieren. Systematische Fehler durch ungenaue Modellierung oder vagem Wissen bleiben jedoch bestehen. Daß Entscheidungsträger mit unsicheren, oft sogar widersprüchlichen Angaben subjektiv urteilen, ist also nicht zu vermeiden. In dieser Arbeit wird gezeigt, wie mit Hilfe eines dreistufigen Bewertungsverfahrens Tragglieder in Qualitätsklassen eingestuft werden können. Abhängig von ihrem mittleren Schadensausmaß, ihrer Strukturbedeutung I (wiederum von ihrem Stellenwert bzw. den Konsequenzen ihrer Schädigung abhängig) und ihrem Prognosefaktor L ergibt sich ihr Versagensrisiko mit. Das Risiko für eine Versagen der Gesamtstruktur wird aus der Topologie ermittelt. Wenn das mittlere Schadensausmaß nicht eindeutig festgelegt werden kann, oder wenn die Material-, Geometrie- oder Lastangaben vage sind, wird im Rahmen "Weitergehender Untersuchungen" ein mathematisches Verfahren basierend auf der Fuzzy-Logik vorgeschlagen. Es filtert auch bei komplexen Ursache-Wirkungsbeziehungen die dominierende Schadensursache heraus und vermeidet, daß mit Unsicherheiten behaftete Parameter für zuverlässige Absolutwerte gehalten werden. Um den mittleren Schadensindex und daraus das Risiko zu berechnen, werden die einzelnen Schadensindizes (je nach Fehlermodus) abhängig von ihrer Bedeutung mit Wichtungsfaktoren belegt,und zusätzlich je nach Art, Bedeutung und Zuverlässigkeit der erhaltenen Information durch Gamma dividiert. Hiermit wurde ein neues Verfahren zur Analyse komplexer Versagensmechanismen vorgestellt, welches nachvollziehbare Schlußfolgerungen ermöglicht

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches

As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Nuclear Power - Control, Reliability and Human Factors

Advances in reactor designs, materials and human-machine interfaces guarantee safety and reliability of emerging reactor technologies, eliminating possibilities for high-consequence human errors as those which have occurred in the past. New instrumentation and control technologies based in digital systems, novel sensors and measurement approaches facilitate safety, reliability and economic competitiveness of nuclear power options. Autonomous operation scenarios are becoming increasingly popular to consider for small modular systems. This book belongs to a series of books on nuclear power published by InTech. It consists of four major sections and contains twenty-one chapters on topics from key subject areas pertinent to instrumentation and control, operation reliability, system aging and human-machine interfaces. The book targets a broad potential readership group - students, researchers and specialists in the field - who are interested in learning about nuclear power

Design, organization and implementation of a methods pool and an application systematics for condition based maintenance

Zunehmender Wettbewerb in der Industrie erfordert immer kürzere Amortisationszeiten von kapitalintensiven Produktionsanlangen. Wesentliche Voraussetzungen für die Realisierung kurzer Amortisationszeiträume sind eine hohe Verfügbarkeit der Anlagen und das Erreichen einer gleichmäßig hohen und konstanten Produktqualität. Eine effiziente Instandhaltungsstrategie unterstützt diese Anforderungen an die Verfügbarkeit und an die Produktqualität, vor allem durch eine geringe Bedarfswartung und zunehmend vorbeugende Instandhaltungsbemühungen. In der Industrie wird hierzu häufig die zustandsbasierte Instandhaltung (Condition Based Maintenance - CBM) angewendet. Die CBM Methode versucht aus Zustandseinschätzung der Maschinen, abgeleitet von verschiedenen Zustandsüberwachungs-Verfahren (Condition Monitoring Technique - CMT) und zerstörungsfreien Prüfungen (Nondestructive Test - NDT), erste Mängel zu identifizieren, bevor sie sich kritisch auf die Produktion auswirken. Ein effektives CBM Programm verlangt eine frühe Fehlererkennung und eine genaue Identifikation der Fehlerattribute. Diese Anforderungen werden in der Industrie heute noch unzureichend erfüllt. Die Ursache liegt vor allem in den hohen Kosten, die sich aufgrund unzureichender Information über die potenziellen Fehler ergeben, sowie in der unzulänglichen Kenntnis oder ungeeigneten Anwendung von verschiedenem CMTs und NDTs begründet. Daher werden im Rahmen dieser Arbeit eine neuartige Toolbox und ein Anwendungskonzept entwickelt, um die Umsetzung eines effektiven CBM Programms in der Automobil-Zulieferindustrie zu unterstützen. Hierbei ist der Ansatz so allgemein gewählt, dass er nicht nur auf das Anwendungsgebiet der Automobilindustrie beschränkt ist, sondern auch auf die allgemeine Herstellungs- oder Produktionsindustrie angewendet werden kann. Die CBM-Toolbox setzt sich aus drei Hauptwerkzeugen zusammen. Das erste Werkzeug fasst statistische Fehler-Analysen zusammen, die die in einem Informationssystem des Betriebes vorhandenen Fehlerdaten auswertet, um die relevanten Informationen tabellarisch bzw. grafisch darzustellen. Das zweite Werkzeug ist eine Wissensdatenbank in der das Expertenwissen über verschiedene CMTs und NDTs verwaltet wird. Dieses Expertenwissen ist so strukturiert, dass zusätzlich zu jeder Methode, ihre Anwendbarkeit, Nachweisbarkeit und Vorteile bzw. Nachteile dargestellt werden. Das dritte Werkzeug ist eine objektbasierte Problem-und-Ursache-Analyse, deren Ergebnis eine tabellarisch dargestellte Problem-Ursache Beziehung von besonderen Maschinenanlagen ist. Diese Hauptwerkzeuge werden durch zwei weitere Werkzeuge, ein Finanzanalyse-Werkzeug und eine Auswahlmatrix ergänzt, die die verschiedenen Entscheidungsmöglichkeiten hinsichtlich der Umsetzbarkeit bewertet.The everyday increasing competition in industry and the compulsion of faster investment paybacks for complex and expensive machinery, in addition to operational safety, health and environmental requirements, take for granted high availability of the production machinery and high and stable quality of products. These targets are reached only if the machinery is kept in proper working condition by utilizing an appropriate maintenance tactic. In this frame of thought, monitoring of machinery systems has become progressively more important in meeting the rapidly changing maintenance requirements of today’s manufacturing systems. Besides, as the pressure to reduce manning in plants increases, so does the need for additional automation and reduced organizational level maintenance. Augmented automation in manufacturing plants has led to rapid growth in the number of machinery sensors installed. Along with reduced manning, increased operating tempos are requiring maintenance providers to make repairs faster and ensure that equipment operates reliably for longer periods. To deal with these challenges, condition based maintenance (CBM) has been widely employed within industry. CBM, as a preventive and predictive action, strives to identify incipient faults before they become critical through structural condition assessment derived from Different condition monitoring techniques (CMT) and nondestructive tests (NDT). An effective CBM program requires early recognition of failures and accurate identification of the associated attributes in a feasible manner. The achievement of this proficiency in industry is still intricate and relatively expensive due to deficient information about the potential failures as well as inadequate knowledge or improper application of different CMTs and NDTs. Accordingly, a new toolbox has been developed to facilitate and sustain effective CBM programs in the automotive supply industry. The CBM toolbox is consisted of three major tools. The first tool is a series of statistical failure analyses which uses the failure history data available in a plant’s information system to generate valuable information in tabulated and graphical postures. The second tool is a repository filled with expert knowledge about different CMTs and NDTs formatted in a way that in addition to the concept of each technique, its applicability, detectability, and its pros and cons are expressed. The third tool is an object based problem and cause analysis whose outcome is tabulated problem-cause relationships associated with particular machinery objects. These major tools are also accompanied by two supplementary tools, a financial analysis tool and a selection matrix, to ensure feasibility of all undertaken decisions while using the toolbox

Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies

Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted

Enhancing Decisions in Prognostics and Health Management Framework

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplified example is developed to illustrate the presented methodology of post-prognostic decision enhancement