This thesis is concerned about the development of computer data base management system for the assessment of masonry bridges. The various techniques of assessment and remedial measures of masonry bridges are outlined, their shortcomings described. A justification for an alternative method of assessment is given. A review of computer data base management systems is carried out. The reasons for adopting data base management systems is given as well as the reasons for choosing a particular type of data base management system. The common faults associated with masonry structures are described and the problems of identifying these faults are described. The part played by the individual components of a masonry arch bridge is given and the significance of faults on the individual components of the structure is described. A detailed description of the type, in general of the data base system chosen is given followed by a detailed description of a special case of the type chosen, which is the system that was used for the project. A description of how the system was developed is given followed by the way the system operates. A detailed description of how the system can be used is then put forward and the problems associated with the development of the system are outlined. Finally, a description of the implications of the system to the practising engineer is given.

<p style="text-align: justify;">Vrednost objekata i njihova upotrebljivost opadaju vremenom usled neadekvatnog održavanja. Uobičajen model korektivnog održavanja postaje neprihvatljiv zbog toga što je ekonomski neodrživ. Reaktivno održavanje se zamenjuje proaktivnim održavanjem čijim se planiranjem nepredvidive intervencije svode na najmanju meru. To je moguće ostvariti uvođenjem savremene organizacije upravljanja održavanjem. Za kvalitetno održavanje neophodno je postojanje baze podataka koja omogućava planiranje i kontrolu aktivnosti na održavanju objekata.</p> / <p style="text-align: justify;">The value of buildings as well as their utilization declines over the time due to<br />inadequate maintenance. Usual method of corrective maintenance becomes<br />unacceptable due to its economic unsustainability. Reactive maintenance is<br />replaced by proactive maintenance the planning of which provides for<br />reduction of unpredictable interventions to the greatest possible extent. This<br />can be achieved by introducing modern organization of maintenance<br />management. With a view to establishing quality maintenance, it is<br />necessary to have the data base which enables planning and supervision of<br />building maintenance activities.</p>

<p>The decision making processes in today's manufacturing systems represent very complex and challenging tasks. The desired flexibility in terms of the functionality of a machine adds more components to the machine. The real time monitoring and reporting generates large streams of data. However the intelligent and real time processing of this large collection of system data is at the core of the manufacturing decision support tools. </p> <p>This thesis outlines the use of Frequent Episodes in Event Sequences and Hidden Markov Modeling of throughput, quality and maintenance data to model the deterioration of performance in the components that make up the manufacturing system. The thesis also introduces the concept of decision points and outlines how to integrate the total cost function in a business model. </p> This thesis deals with the following three topics: <p>First, the component-based data structure of the manufacturing system is outlined especially throughput, quality and maintenance data. In this approach, the manufacturing system is considered as a group of components that interact with each other and with raw materials to produce the manufactured product. This interaction creates a considerable amount of data which can be associated with the relevant components of the system. The relations between the manufacturing components are established on a physical and logical basis. The components properties are clearly defined in database tables specifically created for this application. The thesis also discusses the web services in manufacturing systems and the portable technologies used in plant decision support tools. </p> <p>Second, the thesis presents a novel application of Frequent Episodes in Event Sequences to identify patterns in the deterioration of performance in a component using frequent episodes of operational failures, quality failures and maintenance activities. A Hidden Markov Model (HMM) is used to model each deterioration episode to estimate the states of performance and the transition rates between the states. The thesis compares the results generated by this model to other existing models of component performance deterioration while emphasizing the benefits ofthe proposed model through the use of the plant data.</p> <p>Finally the thesis presents a methodology usmg HMM probability distributions and Bayesian Decision theory framework to provide a set of decisions and recommendations under the condition of data uncertainty. The results of this analysis are then integrated in the plant maintenance business model.</p> <p>It is worthwhile mentioning that to develop the techniques and validate the results in this research; a Manufacturing Execution System (MES) was developed to operate in an automotive engine plant. All the data and results in this research are based on the plant data. The MES which was developed in this research provided significant benefits in the plant and was adapted by many other GM plants around the world.</p> / Thesis / Doctor of Philosophy (PhD)

Maintenance is one area of business that recently has been considered as an activity contributing efficiently to the companies' strategic goals. Understanding the way maintenance could utilise modern technology such as computerised tools or digital data processing is one way to make maintenance profitable. Current research lack appropriate methods for determining data and IT requirements, as well as understanding the way IT could be utilised for enhancing maintenance profitability. This thesis studies and develops tools, methods and theories of how information technology can be utilised for maintenance management in order to reach profitable maintenance. The main research problem is therefore: What are the demands on information technology systems to achieve profitable maintenance? The main problem has been divided into three research questions: RQ1) What are the demands on data and information technology systems for strategic management of maintenance?, RQ2) How can we identify relevant data and information technology systems required to achieve cost-effective maintenance decisions? and RQ3) How can we describe the utilisation of information technology within maintenance? This thesis is based on a systems theory approach, where maintenance is not seen as an isolated activity, but something that interacts, affects and is affected by several other activities, such as production, logistic and quality. Several methods have been used for answering the research questions, but the theory-testing case study method dominates. Main results achieved in the thesis are models and theory for 1) creating a relevant set of data for cost-effective mainte-nance decisions, 2) monitoring the performance of maintenance, suggest invest-ment possibilities and show maintenance contribution on company strategic level, 3) identifying IT systems requirements demanded to achieve profitable maintenance and 4) assessing the IT maturity of a maintenance organisation for the effective utilisation of IT systems. As an illustrative example of computerised maintenance management demands a conceptual decision support model has been developed, which aims at filling the gaps of poor IT coverage for strategic maintenance decision-making. This thesis concludes that the demands of data and IT applications must be connected to the overall maintenance demands, which are reflected in the maintenance goals, purposes and strategy, in order to achieve profitable maintenance. Furthermore, structured methods that ensure the connection between maintenance business goals and data or IT demands are of importance. The ability to make use if IT within maintenance is reflected in the relative IT maturity of the maintenance organisation. Being able to define the IT maturity allows for choosing the most appropriate IT tool to invest in, so that current and future needs of IT support are also covered for with maximum benefit and minimum cost.

Nowadays, by increasing competitions in business, decision-making in maintenance management gets higher priorities. While maintenance in many industries is known as a necessary evil, making cost-effective decisions constructed based on analytical and quantitative assessments improve maintenance position from a cost center into a profit generating one. Within the scope of rotating equipment maintenance, it is vital to consider both economic and technical variables integrated to survive in the competitive market. Taking the role of improving rotating equipment performance within a cost-effective approach into account, this project proposes a consistent model followed by substantial results. The model application can be briefed in improvement of decision-making processes from technical and economic perspectives to direct them into the company profitability aligned with the company goals. Further, it proposes technical and economic indicators and improves utilization of maintenance resources including personnel skills and experience to enhance maintenance performance and develop cost-effective scenarios. Within the model, eMDSS (software) is used as an intelligent facilitator/tool which improves accurate decision-making and identifying and prioritizing maintenance problems. To ensure from the model and its tool applicability, it is applied on a real case study and reliability of the achieved results are tested and approved in detail. In result, the project is successful in evaluating previous technical and economic decisions and selecting the most cost-effective scenario for future with a saving value exceeding 133K€ and profit of 114K€ for the case equipment.

Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application / Datengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage. Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application