RELIABILITY CENTERED MAINTENANCE (RCM) FOR ASSET MANAGEMENT IN ELECTRIC POWER DISTRIBUTION SYSTEM

The purpose of Maintenance is to extend equipment life time or at least the mean time to the next failure. Asset Maintenance, which is part of asset management, incurs expenditure but could result in very costly consequences if not performed or performed too little. It may not even be economical to perform it too frequently. The decision therefore, to eliminate or minimize the risk of equipment failure must not be based on trial and error as it was done in the past. In this thesis, an enhanced Reliability-Centered Maintenance (RCM) methodology that is based on a quantitative relationship between preventive maintenance (PM) performed at system component level and the overall system reliability was applied to identify the distribution components that are critical to system reliability. Maintenance model relating probability of failure to maintenance activity was developed for maintainable distribution components. The Markov maintenance Model developed was then used to predict the remaining life of transformer insulation for a selected distribution system. This Model incorporates various levels of insulation deterioration and minor maintenance state. If current state of insulation ageing is assumed from diagnostic testing and inspection, the Model is capable of computing the average time before insulation failure occurs. The results obtained from both Model simulation and the computer program of the mathematical formulation of the expected remaining life verified the mathematical analysis of the developed model in this thesis. The conclusion from this study shows that it is beneficial to base asset management decisions on a model that is verified with processed, analysed and tested outage data such as the model developed in this thesis

Using a systems approach to analyze the operational safety of dams

Dam systems are arrangements of interacting components that store and convey water for beneficial purposes. Dam failures are associated with extreme consequences to human life, the environment and the economy. Existing techniques for dam safety analysis tend to focus on verifying system performance at the edge of the design envelope. In analyzing the events which occur within the design envelope, linear chain-of-events models are often used to analyze the potential outcomes for the system. These chain-of-events models require that combinations of conditions are identified at the outset of the analysis, which can be very cumbersome given the number of physically possible combinations. Additional complications arising from feedback behaviour and time are not easily overcome using existing tools. Recent work in the industry has begun to focus on systems approaches to the problem, especially stochastic simulation. Given current computational abilities, stochastic simulation may not be capable of analyzing combinations of events that have a low combined probability but potentially extreme consequences. This research focuses on developing and implementing a methodology that dynamically characterizes combinations of component operating states and their potential impacts on dam safety. Automated generation of scenarios is achieved through the use of a component operating states database that defines all possible combinations of component states (scenarios) using combinatorics. A Deterministic Monte Carlo simulation framework systematically characterizes each scenario through a number of iterations that vary adverse operating state timing, impacts and inflows. Component interactions and feedbacks are represented within the system dynamics simulation model. Simulation outcomes provide useful indicators for dam operators including conditional failure rates, times to failure, failure inflow thresholds, and reservoir level exceedance frequencies. Dynamic system response can be assessed directly from the simulation outcomes. The scenario results may be useful to dam owners in emergency decision-making to inform response timelines and to justify the allocation of resources. Results may also help inform the development of improved operating strategies or upgrade alternatives that can reduce the impacts of these extreme events. This work offers a significant improvement in the ability to systematically characterize the potential combinations of events and their consequences

System level risk analysis of electromagnetic environmental effects and lightning effects in aircraft -- steady state and transient

2017 Summer.Includes bibliographical references.This dissertation is an investigation of the system level risk of electromagnetic and lightning effects in aircraft. It begins with an analysis to define a system, and a discussion of emergence as a characteristic of a system. Against this backdrop, risk is defined as an undesirable emergent property of a system. A procedure to translate the system level non-functional attributes to lower level functional requirements is developed. With this foundation, a model for risk analysis, resolution and management is developed by employing the standard risk model. The developed risk model is applied to evaluation of electromagnetic environmental effects and lightning effects in aircraft. Examples are shown to demonstrate the validity of the model. Object Process Methodology and systems thinking principles are used extensively throughout this work. The dissertation concludes with a summary and suggestions for additional work

Resilience of critical structures, infrastructure, and communities

In recent years, the concept of resilience has been introduced to the field of engineering as it relates to disaster mitigation and management. However, the built environment is only one element that supports community functionality. Maintaining community functionality during and after a disaster, defined as resilience, is influenced by multiple components. This report summarizes the research activities of the first two years of an ongoing collaboration between the Politecnico di Torino and the University of California, Berkeley, in the field of disaster resilience. Chapter 1 focuses on the economic dimension of disaster resilience with an application to the San Francisco Bay Area; Chapter 2 analyzes the option of using base-isolation systems to improve the resilience of hospitals and school buildings; Chapter 3 investigates the possibility to adopt discrete event simulation models and a meta-model to measure the resilience of the emergency department of a hospital; Chapter 4 applies the meta-model developed in Chapter 3 to the hospital network in the San Francisco Bay Area, showing the potential of the model for design purposes Chapter 5 uses a questionnaire combined with factorial analysis to evaluate the resilience of a hospital; Chapter 6 applies the concept of agent-based models to analyze the performance of socio-technical networks during an emergency. Two applications are shown: a museum and a train station; Chapter 7 defines restoration fragility functions as tools to measure uncertainties in the restoration process; and Chapter 8 focuses on modeling infrastructure interdependencies using temporal networks at different spatial scales