Master thesis deals with the evaluation of the quality of customers of the company ALVAO, s.r.o., using fuzzy logic. MS Excel and MATLAB programs were used for this evaluation, in which models will be created and programmed. The introductory part of the work is devoted to the theoretical background, which is necessary for understanding the whole issue. The second part of the work presents the company ALVAO, s. r. o.. In the design, main, part of the work are processed fuzzy models and a demonstration of the evaluation of real customers of the company.

The increased energy demand within South Africa has led to continued periods of load shedding. This has had an adverse impact on industry, quality of life and the economy as a whole. A larger requirement for production time, reduced downtime and an enlarged focus on health and safety have steered industry towards a paradigm shift in inspection and maintenance. These activities have progressed from a predominantly time-based (prescriptive) approach towards a risk-based approach. Generally accepted standards like BS EN 16991:2018 and API RP 580 give a comprehensive outline of the basic elements for developing, implementing and maintaining a risk-based inspection program. API RP 581 takes this outline one step further and contains the quantitative methods that support the minimum guidelines presented by API RP 580. Similarly, Kent W. Mühlbauer’s approach has developed a relative risk ranking model for petroleum and gas pipelines, which outlines a qualitative method for representing risk. None of these models are however directly applicable to predicting the failure of pressurised boiler equipment due to the mechanism of corrosion fatigue. API RP 580 / 581 was primarily developed for the oil and gas industry and have practical limitations when applied to pressurised equipment typically found in utilities. BS EN 16991:2018 supplies a framework for utilities, but doesn’t go into the specific detail of how to structure, formulate and apply a risk based management model. The methodology laid out by Kent W. Mühlbauer, while practical and easily implemented, was designed for oil and gas pipelines. A systematic methodology to evaluate the risk associated with specific failure mechanisms in boilers, such as corrosion fatigue, does not exist or is not readily available. A comprehensive risk-based predictive model, using aspects of the abovementioned standards and guides, was developed to demonstrate the predictability of corrosion fatigue in sub-critical boilers. Weightings were assigned to contributory causes to corrosion fatigue, which then allocated relative risk ranks to certain segments within a boiler. Operators and owners of boilers can derive benefit from this model by focusing inspection, maintenance and alteration activities on those equipment locations with the highest relative risk score. / Dissertation (MEng (Metallurgical Engineering))--University of Pretoria 2020. / Eskom Power Plant Engineering Institute (EPPEI) Supervisor: Mr. L. Reddy / Materials Science and Metallurgical Engineering / MEng (Metallurgical Engineering) / Unrestricted

<p>Sewer pipelines are an essential component of wastewater infrastructure and serve as the primary means for transporting wastewater to treatment plants. In the face of increasing demands and declining budgets, municipalities across the US face unprecedented challenges in maintaining current service levels of the 800,000 miles of public sewer pipes. Inadequate maintenance of sewer pipes leads to inflow and infiltration, sanitary sewer overflows, and sinkholes, which threaten human health and are expensive to correct. Accurate condition information from sewers is essential for planning maintenance, repair, and rehabilitation activities and ensuring the longevity of sewer systems. Currently, this information is obtained through visual closed-circuit television (CCTV) inspections and deterioration modeling of sewer pipelines. CCTV inspection facilitates the identification of defects in pipe walls whereas deterioration modeling estimates the remaining service life of pipes based on their current condition. However, both methods have drawbacks that limit their effective usage for sewer condition assessment. For instance, CCTV inspections tend to be labor intensive, costly, and time consuming, with the accuracy of collected data depending on the operator’s experience and skill level. Current deterioration modeling approaches are unable to incorporate spatial information about pipe deterioration, such as the relative locations, densities, and clustering of defects, which play a crucial role in pipe failure. This study attempts to leverage recent advances in deep learning and data mining to address these limitations of CCTV inspection and deterioration modeling and consists of three objectives. </p> <p> </p> <p>The first objective of this study seeks to develop algorithms for automated defect interpretation, to improve the speed and consistency of sewer CCTV inspections. The development, calibration, and testing of the algorithms in this study followed an iterative approach that began with the development of a defect classification system using a 5-layer convolutional neural network (CNN) and evolved into a two-step defect classification and localization framework, which combines a the ResNet34 CNN and Faster R-CNN object detection model. This study also demonstrates the use of a feature visualization technique, called class activation mapping (CAM), as a diagnostic tool to improve the accuracy of CNNs in defect classification tasks—thereby representing a crucial first step in using CNN interpretation techniques to develop improved models for sewer defect identification. </p> <p> </p> <p>Extending upon the development of automated defect interpretation algorithms, the second objective of this study attempts to facilitate autonomous navigation of sewer CCTV robots. To overcome Global Positioning System (GPS) signal unavailability inside underground pipes, this study developed a vision-based algorithm that combines deep learning-based object detection with optical flow for estimating the orientation of sewer CCTV cameras. This algorithm can enable inspection robots to estimate their trajectories and make corrective actions while autonomously traversing pipes. Hence, considered together, the first two objectives of this study pave the way for future inspection technologies that combine automated defect interpretation with autonomous navigation of sewer CCTV robots.</p> <p> </p> <p>The third and final objective of this study seeks to develop a novel methodology that incorporates spatial information about defects (such as their locations, densities, and co-occurrence characteristics) when assessing sewer deterioration. A methodology called Defect Cluster Analysis (DCA) was developed in order to mine sewer inspection reports and identify pipe segments that contain clusters of defects (i.e., multiple defects in proximity). Additionally, an approach to mine co-occurrence characteristics among defects is also introduced (i.e., identification of defects which occur frequently together). Together the two approaches (i.e., DCA and co-occurrence mining) address a key limitation of existing deterioration modeling approaches (i.e., the lack of consideration to spatial information about defects)—thereby leading to the generation of new insights into pipeline rehabilitation decision-making. </p> <p> </p> <p>The algorithms and approaches presented in this dissertation have the potential to improve the speed, accuracy, and consistency of assessing sewer pipeline deterioration, leading to better prioritization strategies for maintenance, repair, and rehabilitation. The automated defect interpretation algorithms proposed in this study can be used to assign the subjective and error-prone task of defect identification to computer processes, thereby enabling human operators to focus on decision-making aspects, such as deciding whether to repair or rehabilitate a pipe. Automated interpretation of sewer CCTV videos could also facilitate re-evaluation of historical sewer inspection videos, which would be infeasible if performed manually. The information gleaned from re-evaluating these videos could generate insights into pipe deterioration, leading to improved deterioration models. The algorithms for autonomous navigation could enable the development of completely autonomous inspection platforms that utilize unmanned aerial vehicles (UAVs) or similar technologies to facilitate rapid assessment of sewers. Furthermore, these technologies could be integrated into wireless sensor networks, paving the way for real-time condition monitoring of sewer infrastructure. The DCA approach could be used as a diagnostic tool to identify specific sections in a pipeline system that have a high propensity for failure due to the existence of multiple defects in proximity. When combined with contextual information (e.g., soil properties, water table levels, and presence of large trees), DCA could provide insights about the likelihood of void formation due to sand infiltration. The DCA approach could also be used to periodically determine how the distribution of defects and their clustering progresses with time and when examined alongside contextual data (e.g., soil properties, water table levels, presence of trees) could reveal trends in pipeline deterioration. </p>

<div>Every year power outages cost billions of dollars and affect millions of people. Historical data shows that between 2000 and 2016, 75% of power outages (in terms of duration) were caused due to severe weather events. Due to climate change these severe weather events are becoming more frequent. The National Association of Regulatory Commissioners have recently emphasized on the importance of building electricity sector's resilience thus ensuring long term reliability and economic benefits for the stakeholders. These severe weather events are called High Impact Low Frequency (HILF) events, which means that these events may not occur every year, but when they happen, the impact is likely to be severe. So, it is imperative that the risk of power outages due to severe weather events and their economic impact is persistent. To mitigate the risk, utilities need to invest heavily so that the impacts due to these HILF events can be minimized. Under this situation, utilities face three key questions (1) where to invest (2) how much to invest and (3) how to justify the investment. Therefore, there is a need to develop a framework for investment related decision-making, which can identify the optimal strategies for minimizing the economic impacts of severe weather induced power outages under different budget conditions. It is equally important to understand the cascading impacts of the sustained power outages during natural disasters before investment can be planned for building resilience in electricity sector. The existing frameworks to access the costs of severe weather induced power outages grossly undermines the overall economic impacts. This research has (1) assessed the economic loss due to severe weather induced power outages in terms of loss of Gross Domestic Product (GDP) and (2) developed a framework for identifying the optimal risk reduction strategies to minimize the economic impact. For assessing the economic impact, this research has adopted Inoperability Input-Output Model (IIM) using 20 years of historical data from the Bureau of Economic Analysis (BEA). The proposed framework has the flexibility to accommodate the risk appetite of the decision maker. The framework can be used by the Investor Owned Utilities (IOUs) for the rate approvals from the State Utility Regulatory Commissions by justifying the importance of their resilience building projects to the State's economy. <br></div>

Previous authors have developed many different types of water pipe condition and failure models using the various methodologies available.  Contrary, current utilities are struggling to maintain their current water infrastructure system, due to the lack of effective prediction tools at hand.  The gap between the methodologies available in academic research and the tools available to current water utilities needs to be addressed.  This paper presents a fuzzy inference prediction model used to forecast the performance rating of individual drinking water pipeline sections (node to node) in which utilities can easily apply to their drinking water infrastructure system. Prior to the development of a prediction model, a through literature and current practice review is completed detailing and summarizing all the available mathematical models.  Following, an infrastructure overview is presented detailing the various pipe materials, lifecycle and failure modes and mechanisms.  A data structure is also detailed which lists all parameters that affect the condition and/or performance of a pipeline.  All of these tools are successfully used to develop a fuzzy inference performance model. The fuzzy inference performance model is considered novel in that it considers close to 30 pipe parameters.  Moreover, the performance model is applied using the Western Virginia Water Authority (WVWA) and the Washington Suburban Sanitary Commission (WSSC) databases to evaluate and verify the predicting results.  Lab testing of several pipe samples is also used to evaluate the model.  The testing consists of a ring bearing test which is used to calculate the rupture modulus of the pipe.  Comparing the original vs. the current rupture modulus can determine the remaining strength of the pipe.  The remaining strength can then be used to assess the performance results predicted by the fuzzy inference model. Further a framework is set forth which utilizes the model's predicted performance ratings to develop deterioration curves which can be used as a tool to forecast and plan future inspection, repair, rehabilitation and replacement of water pipelines.  The deterioration model is made up of a Markov chain approach coupled with a non-optimization technique. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19291号 / 工博第4088号 / 新制||工||1630(附属図書館) / 32293 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 教授 大津 宏康, 准教授 松島 格也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The underground power cable failures introduce challenges to the reliability of underground residential distribution networks. Smart Grid initiatives have interest to improve equipment reliability. Asset management of several utilities are interested towards online cable insulation condition monitoring and health index evaluation. This dissertation demonstrates a new technique for online condition assessment of power cable insulation condition based on capacitor switching transient voltage measurements. While majority of utilities in USA are following corrective maintenance for MV cables, only few utilities have procedures in place for offline preventive maintenance of MV cables. The technique demonstrated in this research will enable all utilities to carry out preventive online monitoring of MV power cables. This dissertation also demonstrates that capacitive test point of cable elbow connector can be used to measure switching transients for power cables in service. This technique can be easily incorporated into existing capacitive test points of cable accessories. This technique has a potential to develop and deploy measurement units for online monitoring of power cables.

During a six-month period, the author has studied, observed and analyzed the situation within Skanska ITN, headquartered in Haga Norra, Sweden. Licenses and supplier relationships and management of these are areas Skanska ITN, which delivers and manages IT services and products to Skanska AB, would like to have investigated and analyzed. The problem reveals itself in the current situation because there is no process for managing software asset management (SAM), i.e. management of licenses and software. There are significant savings to be made when it comes to managing licenses and dealing with suppliers. The purpose of this paper is to identify the processes in dealing with the sourcing of licenses, products and services related to IT and to propose a suitable recommendation that can be adopted by Skanska ITN and the SAM processes. During the study the author found several factors such as lack of responsibility and a defined purpose of the SAM process, which had been defined by the global IT department (GSU IT). These were affecting the parties involved and their work tasks regarding sourcing and purchasing licenses.   Three suggestions for improvements and implementations have been presented and these are: Connect the SAM and purchase relationship with IT roadmaps and Enterprise Architecthure (EA) Initiate a common platform as a forum for sharing documents and agreements which can be stored and managed by the SM3 model Examine and analyze the licenses and agreements within the current situation. To be able to perform this a common policy and purpose must be defined for a SAM tool. / Under en sexmånadersperiod har författaren studerat, observerat samt analyserat situationen på Skanska ITNs huvudkontor i Haga Norra, Stockholm. Licenser och inköpsrelationer och hantering av dessa är områden som Skanska ITN, vilka leverarar och förvaltar IT tjänster och produkter för Skanska AB, vill ha undersökta och analyserade. Problemet grundar sig i att det i dagsläget ej finns en process för att hantera software asset management (SAM), i.e. hantering av mjukvara och licenser. Det finns betydande besparingar att göra när det kommer till att hantera licenser och relationer med leverantörer. Syftet med detta examensarbete är att identifiera potentiella processer för att hantera inköp av licenser, produkter och tjänster relaterade till IT, samt rekommendera en kvalificerad lösning som kan anammas av Skanska ITN och SAM processerna, vilken kan identifiera svagheter och brister och optimera dessa. Under studiens gång fann författaren faktorer såsom brist på ansvarsområden och syfte med den SAM process som hade definierats av den globala IT avdelningen (GSU IT), vilken påverkar inblandade parter och dess arbetsområden. Tre förslag till förbättringar och implementationer har presenterats och dessa är: Koppla SAM och inköpsrelationen mot IT roadmaps och Enterprise Erchitecture (EA) Initiera en gemensam plattform i form av ett forum där delning av dokument och avtal kan lagras vilken kan förvaltas av SM3 modellen Inventera samt analysera de licenser och avtal som existerar i dagsläget och de som bör omförhandlas. För att det ska kunna ske måste ett syfte och direktiv vara aktuellt för vilket SAM verktyg som ska användas.

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Environmental, Social and Governance (ESG) data has been seen as a tool to implement Sustainability in investment companies. This non-financial data has brought along new type of information into the investment process, resulting in a profound transformation for companies. In order to adapt to the new realities of climate change and social challenges, companies must understand that changes in organization processes are essential to address the outcomes that ESG data will bring. Organizational changes are often caused by internal and external factors, allowing organizations to develop new processes and results that can influence the adoption of a new organizational culture approach. This study analyzes the essential organizational changes in terms of structure and culture. The authors used a multi-case study of two Asset management companies and an external group of experts to do so. The case analysis was conducted through a qualitative content analysis based on semi-structured interviews with nine employees and two external experts within the Asset Management industry. The study results show that ESG plays a vital role in organizational development, forcing structural changes and a new approach toward organizational culture in Asset Management companies.