Management decisions in complex industrial facilities usually consider both the economic and environmental aspects of the plant's performance. For nuclear power plants (NPPs), safety is also a very substantial issue. The objectives of this dissertation are to develop and demonstrate a novel useful conceptual model that could be used to allocate maintenance funds for a nuclear power plant in such a way as to meet all specified safety requirements and objectives, while achieving a high degree of economic performance. The model is based on the general theory that the reliability of a plant at any time is a function of its initial reliability and the maintenance history of the individual plant components (Smith, 1997). Such a model can assist in evaluating strategic management decisions regarding allocation of funds for nuclear power plant maintenance. It could be used as a simulation tool; various scenarios could be studied to answer "what if" questions. Simulations of this type will allow a better understanding of the relationship between maintenance, economic performance, and safety, and consequently will lead to better decision making. The novelty of this model is tied to the intimate relationship that it develops between maintenance activities at a nuclear plant, and their relationship to prescribed safety requirements and to the economic performance of that plant.

Management decisions in complex industrial facilities usually consider both the economic and environmental aspects of the plant's performance. For nuclear power plants (NPPs), safety is also a very substantial issue. The objectives of this dissertation are to develop and demonstrate a novel useful conceptual model that could be used to allocate maintenance funds for a nuclear power plant in such a way as to meet all specified safety requirements and objectives, while achieving a high degree of economic performance. The model is based on the general theory that the reliability of a plant at any time is a function of its initial reliability and the maintenance history of the individual plant components (Smith, 1997). Such a model can assist in evaluating strategic management decisions regarding allocation of funds for nuclear power plant maintenance. It could be used as a simulation tool; various scenarios could be studied to answer "what if" questions. Simulations of this type will allow a better understanding of the relationship between maintenance, economic performance, and safety, and consequently will lead to better decision making. The novelty of this model is tied to the intimate relationship that it develops between maintenance activities at a nuclear plant, and their relationship to prescribed safety requirements and to the economic performance of that plant.

This research focuses on finding the optimal maintenance policy for an item with varying failure behavior. We analyze several types of item failure rates and develop methods to solve for optimal maintenance schedules. We also illustrate nonparametric modeling techniques for failure rates, and utilize these models in the optimization methods. The general problem falls under the umbrella of stochastic optimization under uncertainty. / text

Maintenance management is a relevant issue in modern technical systems due to its financial, safety, and environmental implications. The need to rely on physical assets makes maintenance a necessary evil, which, on the other hand, allows achieving a high quality of end products, or services, and a safety level that is adequate for the regulatory requirements. The advent of the fourth industrial revolution offers meaningful opportunities to improve maintenance management; technologies such as Cyber-Physical Systems, the Internet of Things, and cloud computing enable realizing modern infrastructure to support decisions with advanced analytics. In this thesis, the optimization of maintenance policies is tackled in this renewed technological context. The research methods employed in this thesis include interviewing of subject experts, literature research, and numerical experiments. Mathematical modelling is used to model network effects in complex technical systems, and simulations are used to validate the proposed models and methodologies. The problem of maintenance policies comparison is addressed in one of the publications; using the proposed bi-objective analysis, an effective maintenance policy was identified. Maintenance of complex systems organized in a networked fashion is studied in another project, where maintenance costs and system performances are considered. The proposed model allowed to identify a set of non-dominated (in the Pareto sense) maintenance policies, and an efficient resolution procedure was developed. The possibility to use a digital twin to replicate a Cyber-Physical System for maintenance policies optimization is addressed in another publication. The main hurdles in realizing such a complex infrastructure are analyzed, and managerial implications are presented. Finally, following a qualitative research approach, the opportunities offered by additive manufacturing are identified and presented in a book chapter. The opportunities for both maintenance efficiency gains and new business models are identified and discussed.

The risk-based life-cycle management of engineering systems in a nuclear power plant is intended to ensure safe and economically efficient operation of energy generation infrastructure over its entire service life. An important element of life-cycle management is to understand, model and forecast the effect of various degradation mechanisms affecting the performance of engineering systems, structures and components. The modeling of degradation in nuclear plant components is confounded by large sampling and temporal uncertainties. The reason is that nuclear systems are not readily accessible for inspections due to high level of radiation and large costs associated with remote data collection methods. The models of degradation used by industry are largely derived from ordinary linear regression methods. The main objective of this thesis is to develop more advanced techniques based on stochastic process theory to model deterioration in engineering components with the purpose of providing more scientific basis to life-cycle management of aging nuclear power plants. This thesis proposes a stochastic gamma process (GP) model for deterioration and develops a suite of statistical techniques for calibrating the model parameters. The gamma process is a versatile and mathematically tractable stochastic model for a wide variety of degradation phenomena, and another desirable property is its nonnegative, monotonically increasing sample paths. In the thesis, the GP model is extended by including additional covariates and also modeling for random effects. The optimization of age-based replacement and condition-based maintenance strategies is also presented. The thesis also investigates improved regression techniques for modeling deterioration. A linear mixed-effects (LME) regression model is presented to resolve an inconsistency of the traditional regression models. The proposed LME model assumes that the randomness in deterioration is decomposed into two parts: the unobserved heterogeneity of individual units and additive measurement errors. Another common way to model deterioration in civil engineering is to treat the rate of deterioration as a random variable. In the context of condition-based maintenance, the thesis shows that the random variable rate (RV) model is inadequate to incorporate temporal variability, because the deterioration along a specific sample path becomes deterministic. This distinction between the RV and GP models has profound implications to the optimization of maintenance strategies. The thesis presents detailed practical applications of the proposed models to feeder pipe systems and fuel channels in CANDU nuclear reactors. In summary, a careful consideration of the nature of uncertainties associated with deterioration is important for credible life-cycle management of engineering systems. If the deterioration process is affected by temporal uncertainty, it is important to model it as a stochastic process.

The risk-based life-cycle management of engineering systems in a nuclear power plant is intended to ensure safe and economically efficient operation of energy generation infrastructure over its entire service life. An important element of life-cycle management is to understand, model and forecast the effect of various degradation mechanisms affecting the performance of engineering systems, structures and components. The modeling of degradation in nuclear plant components is confounded by large sampling and temporal uncertainties. The reason is that nuclear systems are not readily accessible for inspections due to high level of radiation and large costs associated with remote data collection methods. The models of degradation used by industry are largely derived from ordinary linear regression methods. The main objective of this thesis is to develop more advanced techniques based on stochastic process theory to model deterioration in engineering components with the purpose of providing more scientific basis to life-cycle management of aging nuclear power plants. This thesis proposes a stochastic gamma process (GP) model for deterioration and develops a suite of statistical techniques for calibrating the model parameters. The gamma process is a versatile and mathematically tractable stochastic model for a wide variety of degradation phenomena, and another desirable property is its nonnegative, monotonically increasing sample paths. In the thesis, the GP model is extended by including additional covariates and also modeling for random effects. The optimization of age-based replacement and condition-based maintenance strategies is also presented. The thesis also investigates improved regression techniques for modeling deterioration. A linear mixed-effects (LME) regression model is presented to resolve an inconsistency of the traditional regression models. The proposed LME model assumes that the randomness in deterioration is decomposed into two parts: the unobserved heterogeneity of individual units and additive measurement errors. Another common way to model deterioration in civil engineering is to treat the rate of deterioration as a random variable. In the context of condition-based maintenance, the thesis shows that the random variable rate (RV) model is inadequate to incorporate temporal variability, because the deterioration along a specific sample path becomes deterministic. This distinction between the RV and GP models has profound implications to the optimization of maintenance strategies. The thesis presents detailed practical applications of the proposed models to feeder pipe systems and fuel channels in CANDU nuclear reactors. In summary, a careful consideration of the nature of uncertainties associated with deterioration is important for credible life-cycle management of engineering systems. If the deterioration process is affected by temporal uncertainty, it is important to model it as a stochastic process.

The purpose of this paper is to present a sample of how Swedish manufacturing companies deal with equipment downtime cost, and further how they analyze its reduction. The study was performed by conducting a web-based survey within Swedish firms that have at least 200 employees. The main results obtained from the investigation show that the estimated downtime cost constitute about 23.9 % from the total manufacturing cost ratio, and 13.3 % from planned production time. Additionally, the hourly cost of downtime, whether planned or unplanned, is relatively high. However, there is a shortage of systematic models that capable to trace the individual cost imposed by downtime events. This lack was shown apparently whilst 83 % of surveyed companies they do not have any complete model adapted for quantifying their downtime costs. Moreover, only few companies develop their cost accounting methods such as, activity-based costing (ABC) and resource consumption accounting (RCA) to assimilate and reveal the real costs that associated with planned and unplanned stoppages. Still, the general pattern of downtime cost calculation allocated to direct labor and lost capacity cost. On the other hand, the attempts of decreasing downtime events and thus costs were based on schedule maintenance tactics that supported by overall equipment effectiveness (OEE) tool, as an indicator for affirming improvements. Nonetheless, the analysis indicates the need for optimized maintenance tactics by incorporating reliability-centered maintenance (RCM) and total productive maintenance (TPM) into companies’ maintenance systems. The maintenance role of reducing downtime impacts not highly recognized. Furthermore, the same analysis shows the requirement for better results of performance measurement systems is by implementing total equipment effectiveness performance tool (TEEP). The advantage of such tool is to provide the impact index of planned stoppages in equipment utilization factor. Finally, the lack of fully integrated models for assessing the downtime costs and frameworks for distinguishing the difference between planned and unplanned stoppages are the main reasons behind the continuation of cost in ascending form. Due to that, the improvements will emphasize on areas with less cost saving opportunities. As a result, this will affect the production efficiency and effectiveness which in return has its influence on costs and thereby profits margin.

In power system asset management, component data is crucial for decision making. This thesis mainly focuses on two aspects of asset data: data quality and data availability. The quality level of data has a great impact on the optimality of asset management decisions. The goal is to quantify the impact of data errors from a maintenance optimization perspective using random population studies. In quantitative terms, the impact of data quality can be evaluated financially and technically. The financial impact is the total maintenance cost per year of a specific scenario in a population of components, whereas the technical impact is the loss of a component's useful technical lifetime due to sub-optimal replacement time. Using Monte-Carlo simulation techniques, those impacts are analyzed in a case study of a simplified random population of independent and non-repairable components. The results show that missing data has a larger impact on cost and replacement year estimation than that of under- or over-estimated data. Additionally, depending on problem parameters, after a certain threshold of missing data probability, the estimation of cost and replacement year becomes unreliable. Thus, effective decision making for a certain population of components requires ensuring a minimum level of data quality. Data availability is another challenge that faces power system asset managers. Data can be lacking due to several factors including censoring, restricted access, or absence of data acquisition. These factors are addressed in this thesis from a decision making point of view through case studies at the operation and maintenance levels. Data censoring is handled as a data quality problem using a Monte-Carlo simulation. While the problems of restricted access and absence of data acquisition are studied using event trees and multiphysics modelling.  While the quantitative data quality problem can be abstract, and thus applicable to different types of physical assets, the data availability problem requires a case-by-case analysis to reach an effective decision making strategy. / <p>QC 20210528</p> / CPC5

Asset management is getting increasingly important in nearly all fields, especially inthe electric power engineering. It is mainly due to the following two reasons. First isthe high investment cost include the design cost, construction cost, equipment costand the high maintenance cost. Another reason is that there is always a high penaltyfee for the system operator if an interruption happened in the system. Besides, due tothe deregulation of electricity market in these years, the electricity utilities are payingmore attentions to the investment and maintenance cost. And one of their main goalsis to maximize the maintenance performance. So the challenge for the systems is toprovide high-reliability power to the customs and meanwhile be cost-effective for thesuppliers. Reliability Centered Asset Management (RCAM) is one of the bestmethods to solve this problem.The basic RCAM method is introduced first in this thesis. The model includes themaintenance strategy definition, the maintenance cost calculation and an optimizationmodel. Based on the basic model some improvements are added and a new model isproposed. The improvements include the new improvement maintenance strategy,increasing failure rate and a new objective function. The new model is also able toprovide a time-based maintenance plan.The simulation is done to a Swedish distribution system-Birka system by GAMS. Theresults and a sensitivity analysis is presented. A maintenance strategy for 58components and in 120 months is finally found. The impact on the changing failurerate is also shown for the whole peroid. / Kapitalförvaltning har inom alla områdem blivit allt viktigare, speciellt inomelkraftsteknik. Det beror i huvudsak av två orsaker. Den första är storinvesteringskostnad, vilket inkluderar design, konstruktion, utrustning och underhåll.Den andra är den höga straffavgiften för system operatören vid elavbrott. Dessutom,på grund av den nyligen avreglerade elmarknaden, så fäster elföretagen meruppmärksamhet på investerings och underhållskostnader. En av deras huvudmål är attmaximera underhållsprestandan. Så utmaningen för operatörerna är att levereratillförlitlig elkraft till kunder, samtidigt vara kostnadseffektiva mot leveratörer.Reliability Centered Asset Management (RCAM) är bland de bästa metoderna för attlösa detta problem. En enklare RCAM metod är introducerad först i denna rapport.Modellen inkluderar en underhållsstrategi-definition, underhållskostnad-kalkyl och enIIoptimiserings modell. Grundad på denna enklare modell, andra förbättringar ärtillagda och en ny modell är föreslagen. Förbättringarna inrymmer en nyunderhållsstrategi, ökad felfrekvens och en ny målfunktion. Den nya modellentillhandahåller också en tidsbaserad underhållsplan.

<p>Maximum asset performance is one of the major goals for electric power system managers. To reach this goal minimal life cycle cost and maintenance optimization become crucial while meeting demands from customers and regulators. One of the fundamental objectives is therefore to relate maintenance and reliability in an efficiently and effectively way, which is the aim of several maintenance methods such as the Reliability Centered Maintenance method (RCM). Furthermore, this necessitates the determination of the optimal balance between preventive and corrective maintenance to obtain the lowest total cost.</p><p>This thesis proposes methods for defining the importance of individual components in a network with respect to total interruption cost. This is a first step in obtaining an optimal maintenance solution. Since the methods consider several customer nodes simultaneously, they are especially suitable for network structures that serve many purposes/customers e.g. transmission and distribution networks with more than one load point. The major results are three component reliability importance indices, which are applied in two case studies. The first case study is based on a network in the Stockholm area. The second case study is performed for one overhead line system in the rural parts of Kristinehamn. The application studies demonstrate that the indices are possible to implement for existing electrical networks and that they can be used for maintenance prioritization. Consequently these indices constitute a first step in the overall objective of a maintenance optimization method.</p><p>The computations of the indices are performed both with analytical and simulation based techniques. Furthermore, the indices can be used to calculate the component contribution to the total system interruption cost. The approach developed for the importance indices can be utilized in any multi-state network that can be measured with one performance indicator.</p>