The development of software products is a massive undertaking, and organisations have to manage all artefacts involved in the process. Managing such artefacts that, in many cases, become crucial assets is important for success. Recognising assets and letting them (unintentionally) degrade can result in maintainability problems. Thus, there is a need to create a structured and organised body of knowledge that can guide practitioners and researchers to deal with the assets during the product/service life-cycle. This includes, but is not limited to, what steps are needed to understand the assets’ degradation, investigating and examining the existing methods and metrics on how to estimate degradation and understanding the implication of assets’ value and degradation. This licentiate’s main objective is contributing to the software engineering field by providing a different perspective on assets focusing on assets’ value for the organisation. We have used literature reviews, focus groups, case study, and sample study to address this objective. The collected data is from peer-reviewed work, collaboration with five company partners, and 31 OSS from Apache Foundation. First, we have defined the concept and terminology in a position paper. We havecreated an asset management taxonomy based on a literature review and focus groups– fours focus groups conducted in 2019 with 29 participants. The extracted assets represent not only the stages of software development, from requirements to verificationand validation, but also operational and organisational perspectives. The taxonomy wascreated to be extendable as the field evolves and matures. Then, we have performed a more in-depth investigation of selected asset types. As a part of studying assets, in a case study, we present the impact of bug-fixing,refactorings, and new development to investigate how source code degrades. In anothersample study, we examine the longevity of specific source-code related issues in 31OSS from Apache Foundation using statistical analysis. The work done in this licentiate includes: defining the asset concept and relatedterminology, identifying assets and creating a taxonomy of assets, presenting the preliminary investigation of tools and methods to understand source-code and architecturerelated asset degradation. We conclude that a good understanding of the relevant assets for the inception,planning, development, evolution, and maintenance of software-intensive products andservices is necessary to study their value degradation. Our work builds on currentmethods and details the underlying concepts attempting to homogenise definitions andbring the areas of assets and degradation together. A natural progression of our workis to investigate the measurements to evaluate the degradation of assets. This licentiate thesis starts investigating the value degradation of source-code related assets. We planto continue investigating the degradation of architecture in our future work.

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Under the evolving environment, a utility company is required to improve the operation and maintenance of its physical assets usually in the forms of an asset management program. This paper proposes an organisational learning model for the utility companies with respect to the asset management activities. CommonKADS is utilised as a tool to capture the knowledge associated with managing the assets from the learning processes of the utility company. A case study of Bangpakong power plant in Thailand is presented. The results show that by applying the proposed methodologies, the learning processes within the utility companies can be categorised and explained by five major learning steps of breakdown, corrective, preventive, predictive, and proactive maintenances.

Asset management of force main infrastructure entails accurate prediction of the condition of the system to operate and maintain at the lowest overall costs. In this thesis report, guidelines for asset management of force main infrastructure is provided by synthesizing the trends observed in the inspection, condition assessment and renewal engineering strategies. Furthermore, this thesis focuses on development of a centralized web-based platform for advanced asset management of force main infrastructure. The key components involved in this comprehensive asset management of the force main infrastructure are data management, model implementation and information visualization. The thesis depicts various aspects involved in developing a web-based application for utilities that store, collect and analyze the data in dissimilar methods. A risk assessment model employed by a utility to prioritize the assets for renewal is demonstrated with various utilities' data. Consequently, the model is published as geo-processing services through ESRI ArcGIS Server. A visualization tool is developed for individual utilities that interacts with the geo-processing services and renders a web-based interactive map to visualize the model results. A drupal website (www.pipeid.org) is developed to support the data collection and model dissemination process. / Master of Science

High-quality drinking water and wastewater systems are essential to public health, business, and quality of life in the United States. Even though the current performance of these systems is moderate, the concern is about the future performance. Planning can be done for improvement once the current performance of utilities is evaluated, and areas with a scope of improvement are identified. Benchmarking and performance evaluation are key components in the process of continuous improvement for utility's performance. Benchmarking helps utilities make policies and programmatic decisions that reduce operational expenses and increase productivity by understanding areas of underperformance, understanding customer needs, developing future plans, and setting goals. This study establishes a strong case for implementing benchmarking methodologies among utilities to evaluate and improve performance. There are many initiatives on performance benchmarking of utilities but a few of them focuses on one or few area of performance. There are a few initiatives which use subjective indicators. Additionally, consultants visit the utilities for performance evaluation. This research focuses on creating a web-based benchmarking platform for performance evaluation using holistic and quantitative indicators. Practical and robust methodologies are used and the research presents the current performance comparisons among utilities for areas that impact overall utility's performance. Web based benchmarking consists of two major parts -- data collection and result visualization. A major contribution from this study is the creation of an online performance benchmarking database. With time more data will be collected which will provide utilities an access to a better database for performance evaluation. The future work in this research will be analyzing the data and results for each participant for each set of indicators, and finding possible reasons for under performance, followed by suggesting solutions for improvement using the best practices. / Master of Science

There is an unprecedented need to manage our deteriorating water infrastructure systems effectively to mitigate the enormous consequences of their premature failure such as loss of service, money, time, damage to other infrastructure, and damage to property. Most of the water utilities understand this need and are implementing asset management approaches and technologies to increase the overall service life of their assets. However, to indeed achieve sustainable water infrastructure systems, there is a requirement to implement a risk-based asset management program which provides a more comprehensive approach to manage these aging assets. A risk-based asset management program assesses and manages the risk of failure associated with the water infrastructure assets and helps water utilities in prioritizing their assets for renewal. This program identifies the critical assets for renewal and saves the money and time invested in the renewal of 'not so critical' assets. This research incorporates an extensive literature and practice review on risk-based asset management of pipes, valves, and hydrants. The risk-based asset management consist of the following four major components: (1) understanding the deterioration modes and mechanisms, (2) implementing risk assessment and management approaches, (3) implementing condition assessment approaches and technologies, and (4) implementing asset renewal approaches and technologies. This research aims to provide enhanced guidelines based on the EPA 10 step asset management program which will help water utilities in developing a risk-based asset management program as well as in improving their existing asset management program. This research combines the in-depth knowledge gained through a state-of-the-art literature review and practice review. The practice review is conducted to capture the real world application of the risk-based asset management through interviews with the water utilities across the united states. This research has also compiled the knowledge gained by already published case studies to provide a more comprehensive overview of the current practices and trend in the risk-based asset management of drinking water pipelines, valves, and hydrants. / Master of Science / America’s drinking water infrastructure is deteriorating and there is an unprecedented need to manage our deteriorating water infrastructure systems effectively to mitigate the enormous impacts of their premature failure such as loss of service, money, time, damage to other infrastructure, and damage to property. In order to achieve sustainable water infrastructure systems, there is a requirement to implement a risk-based asset management program which is a comprehensive approach to manage these aging assets. A risk-based asset management program assesses and manages the risk of failure associated with the water infrastructure assets and helps water utilities in prioritizing their assets for renewal. This program identifies the critical assets for renewal and saves the money and time invested in the renewal of “not so critical” assets. This research aims to provide enhanced guidelines based on the EPA 10 step asset management program which will help water utilities in developing a risk-based asset management program as well as in improving their existing asset management program. This research combines the in-depth knowledge gained through a state-of-the-art literature review and practice review. The practice review is conducted to capture the real-world application of the risk-based asset management through interviews with the water utilities across the united states. This research has also compiled the knowledge gained by already published case studies to provide a more comprehensive overview of the current practices and trend in the risk-based asset management of drinking water pipelines, valves, and hydrants.

Yes / This paper describes the design, development, deployment and performance assessment of a prototype system for monitoring the 'health' of a water distribution network based on the temperature distribution and time-dependent variations in temperature across the network. It has been found that the water temperature can reveal unusual events in a water distribution network, indicated by dynamic variations in spatial temperature differential. Based on this indication it is shown how patterns of changes in the water temperature can be analysed using AQUIS pipeline distribution software and used in conjunction with hydraulic (e.g. flow and pressure) sensors to indicate the state of ¿health¿ of the network during operation.

Facility management involves a variety of processes with a large amount of data for managing and maintaining facilities. Processing and prioritizing work orders constitute a big part of facility management, given the large number of work orders submitted daily. Current practices for prioritizing work orders are mainly user-driven and lack consistency in collecting, processing, and managing a large amount of data. Decision-making methods have been used to address challenges such as inconsistency. However, they have challenges, including variations between comparisons during the actual prioritization task as opposed to those outside of the maintenance context. Data-driven methods can help bridge the gap by extracting meaningful and valuable information and patterns to support future decision-makings. Through a review of the literature, interviews, and survey questionnaires, this research explored different industry practices in various facilities and identified challenges and gaps with existing practices. Challenges include inconsistency in data collection and prioritizing work orders, lack of data requirements, and coping strategies and biases. The collected data showed the list of criteria and their rankings for different facilities and demonstrated the possible impact of facility type, size, and years of experience on criteria selection and ranking. Based on the results, this research proposed a methodology to automate the process of prioritizing work orders using Neural Networks. The research analyzed the work order data obtained from an educational facility, explained data cleaning and preprocessing approaches, and provided insights. The data exploration and preprocessing revealed challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for work orders' execution, and lack of correlation between collected criteria and the schedule. Through hyperparameter tuning, the optimum neural network configuration was identified. The developed neural network predicts the schedule of new work orders based on the existing data. The outcome of this research can be used to develop requirements and guidelines for collecting and processing work order data, improve the accuracy of work order scheduling, and increase the efficiency of existing practices using data-driven approaches. / Doctor of Philosophy / Facility Management (FM) is a profession that integrates various disciplines to ensure the comfort and safety of the occupants, efficiency of the built environment, and functionality of the building while meeting the main objectives of the owners. It involves various functions, including space management, communication, contract management, inspection, etc. Among many of these FM functions, maintenance-related tasks occupy 79% of the facility managers' responsibilities and %60 of the building cost in its whole lifecycle (design, construction, and operation). Prioritizing and processing work orders constitute a big part of facility maintenance management and requires a large amount of information submitted with hundreds of orders that need to be prioritized and turned into actions on a daily basis. Although vast amounts of work orders are submitted daily, the process of prioritizing orders has been done manually or partially through management systems rendering the process very challenging. The existing practices are highly dependent on the extent of knowledge, experience, and judgment of responsible staff available, are impacted by human cognitive workload and coping strategies and are challenged by inconsistency in data collection and uncertainty in decision-making. Delays in processing work orders can lead to asset downtimes and failure impacting occupants' comfort, health, and safety while increasing the cost of operation. Additionally, based on the results of previous studies, the alternative comparison for prioritizing work orders varies and is more realistic when performed during the actual work order prioritization task as opposed to outside of the maintenance context. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have provided opportunities to benefit from the historical data collected and stored by the facilities. Since a large number of work orders are generated and stored by facilities, such methods can be used to address the challenges with existing practices to reduce errors, downtimes, and asset failures and improve the operation of the buildings by supporting automation within the systems. This dissertation first aims to explore the existing practices for processing and prioritizing work orders and identifying their gaps and challenges. Second, it investigates the implementation of Artificial Neural Networks (ANNs) to automate the prioritization of future work orders. The ANN is one type of machine learning model which reflects and mimics the behavior of the human brain to understand the relationship between a set of data allowing computer programs to solve complex problems. This research will improve the existing practices for processing work orders by allowing the automation of future work order prioritization. It also provides the basis for the development of data requirements to further support existing practices.

Thesis (D.Tech. - Electrical Engineering, Dept. of Process Control and Computer Systems, Faculty of Engineering and Technology)--Vaal University of Technology. / In recent years, industrial plant maintenance has changed dramatically. These changes are due to a considerable increase in the number and variety of physical plant assets, increased design complexity, new maintenance techniques and changing perspectives regarding on how to perform maintenance effectively. Managers at modern process plants are becoming increasingly aware of the extent to which equipment failure affects safety and the environment. Process plant personnel are limited in their ability to accurately and consistently evaluate the health of plant assets. Due to poor record keeping, maintenance staff often has little defence against aging equipment and asset failures. As a result companies have undertaken to implement planned equipment maintenance schedules and install new technology to allow for efficient tracking and analysing of equipment health across the board. The introduction of an integrated asset management solution is presented in this thesis. The integrated asset management solution will assist maintenance staff to cost-effectively predict the probability of asset failure prior to the occurrence of any actual plant incidents. The integrated solution documented in this thesis will be implemented at the Sasol Solvents site to enhance plant availability, maximum up time for all plant assets and plant safety. Strategic Asset Management Inc. (SAMI) uses the Operational Reliability Maturity Continuum model to improve profitability, efficiency and equipment reliability. The SAMI empirical model employs various stages to address improved performance and asset management and was used as a guideline to develop an integrated solution to optimise plant performance and profits. The integrated asset management solution, documented in this thesis, was developed with the intended function of bringing information from diverse plant based systems and field equipment to the maintenance personnel in an understandable interface so that the information can be used to improve the reliability and availability of all plant assets.