While researchers have concentrated on the optimization of joint redundancy and maintenance mechanism, maintenance in computing systems is quite different from that in traditional systems. Considering a routine monitoring and inspection mechanisms is conducted to detect component status and trigger repair process, this paper pays attention to the optimization problem of joint redundancy and inspection-based maintenance mechanism. After conducting steady state analysis on subsystems using inspection-based maintenance, shared repair facility and component redundancy, optimization model is built to search appropriate system structure and maintenance policy which maximizes system performance while meeting availability and cost constraints. Due to the complexity of uncertain optimization model, genetic algorithm is used to search optimal solution, using triple-element encoding mechanism and specifically designed operators. Illustrative examples are conducted to show that the optimization model and corresponding solution technique could be used to search optimal system configuration under given constraints and different cost constraints would lead to different optimization result while meeting availability constraints.

When a system is composed of highly reliable elements, exact reliability quantification may be problematic, because computer accuracy is limited. Inaccuracy can be due to different aspects. For example, an error may be made when subtracting two numbers that are very close to each other, or at the process of summation of many very different numbers, etc. The basic objective of this paper is to find a procedure, which eliminates errors made by PC when calculations close to an error limit are executed. Highly reliable system is represented by the use of directed acyclic graph which is composed from terminal nodes, i.e. highly reliable input elements, internal nodes representing subsystems and edges that bind all of these nodes. Three admissible unavailability models of terminal nodes are introduced, including both corrective and preventive maintenance. The algorithm for exact unavailability calculation of terminal nodes is based on merits of a high-performance language for technical computing MATLAB. System unavailability quantification procedure applied to a graph structure, which considers both independent and dependent (i.e. repeatedly occurring) terminal nodes is based on combinatorial principle. This principle requires summation of a lot of very different non-negative numbers, which may be a source of an inaccuracy. That is why another algorithm for exact summation of such numbers is designed in the paper. The summation procedure uses benefits from a special number system with the base represented by the value 232. Computational efficiency of the new computing methodology is compared with advanced simulation software. Various calculations on systems from references are performed to emphasize merits of the methodology.

This research work presents the development and application of a preventive maintenance programme in an oil refinery plant. This study was carried out in a period of three years in one of the main Italian refineries. The research has been focused on the turnaround process that has been analysed by two different approaches: a risk-based method and an innovative criticality index. Both have been used as decision tools for clearly assessing the maintenance tasks and equipment to include in the process. The results have highlighted a clear improvement in terms of resources usage optimisation, outage duration, production loss and total costs reduction and increase of the interval between two shutdowns.

This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.

This paper studies a multi-objective maintenance optimization embedded within the imperfect preventive maintenance (PM) for one single-unit system subject to the dependent competing risks of degradation wear and random shocks. We consider two kinds of random shocks in the system: 1) fatal shocks that will cause the system to fail immediately, and 2) nonfatal shocks that will increase the system degradation level by a certain cumulative shock amount. Also, an improvement factor in the form of quasi-renewal sequences is introduced to modulate the imperfect maintenance by raising the degradation critical threshold proportionally. Finally, the two decision variables for maintenance scheduling, the number of PMs to replacement, and the initial PM interval, are determined by simultaneously maximizing the system asymptotic availability, and minimizing the system cost rate using the fast elitist non-dominated Sorting Genetic Algorithm (NSGA-II). Sensitivity analysis for two parameters, including imperfect PM degree, and quasi-renewal coefficient of imperfect PM interval, is performed to provide insight into the behavior of the proposed maintenance policies. The comparison results show that the optimization solution is consistent between one-objective and multi-objective optimization, and the Pareto frontier for the maintenance optimization problem can provide alternative solutions according to customer preference and resource constraints.

This paper presents the optimization of design and test policies of safety instrumented systems using MooN voting redundancies by a multi-objective genetic algorithm. The objectives to optimize are the Average Probability of Dangerous Failure on Demand, which represents the system safety integrity, the Spurious Trip Rate and the Lifecycle Cost. In this way safety, reliability and cost are included. This is done by using novel models of time-dependent probability of failure on demand and spurious trip rate, recently published by the authors. These models are capable of delivering the level of modeling detail required by the standard IEC 61508. Modeling includes common cause failure and diagnostic coverage. The Probability of Failure on Demand model also permits to quantify results with changing testing strategies. The optimization is performed using the multi-objective Genetic Algorithm NSGA-II. This allows weighting of the trade-offs between the three objectives and, thus, implementation of safety systems that keep a good balance between safety, reliability and cost. The complete methodology is applied to two separate case studies, one for optimization of system design with redundancy allocation and component selection and another for optimization of testing policies. Both optimization cases are performed for both systems with MooN redundancies and systems with only parallel redundancies. Their results are compared, demonstrating how introducing MooN architectures presents a significant improvement for the optimization process.

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability.

This paper introduces a new development for modelling the time-dependent probability of failure on demand of parallel architectures, and illustrates its application to multi-objective optimization of proof testing policies for safety instrumented systems. The model is based on the mean test cycle, which includes the different evaluation intervals that a module goes periodically through its time in service: test, repair and time between tests. The model is aimed at evaluating explicitly the effects of different test frequencies and strategies (i.e. simultaneous, sequential and staggered). It includes quantification of both detected and undetected failures, and puts special emphasis on the quantification of the contribution of the common cause failure to the system probability of failure on demand as an additional component. Subsequently, the paper presents the multi-objective optimization of proof testing policies with genetic algorithms, using this model for quantification of average probability of failure on demand as one of the objectives. The other two objectives are the system spurious trip rate and lifecycle cost. This permits balancing of the most important aspects of safety system implementation. The approach addresses the requirements of the standard IEC 61508. The overall methodology is illustrated through a practical application case of a protective system against high temperature and pressure of a chemical reactor.

This paper discusses the use of genetic algorithms (GA) within the area of reliability, availability, maintainability and safety (RAMS) optimization. First, the multi-objective optimization problem is formulated in general terms and two alternative approaches to its solution are illustrated. Then, the theory behind the operation of GA is presented. The steps of the algorithm are sketched to some details for both the traditional breeding procedure as well as for more sophisticated breeding procedures. The necessity of affine transforming the fitness function, object of the optimization, is discussed in detail, together with the transformation itself. In addition, how to handle constraints by the penalization approach is illustrated. Finally, specific metrics for measuring the performance of a genetic algorithm are introduced.

This chapter summarizes the state-of-the-art and state-of-practice on the applications of safety and reliability approaches in the Power Sector. The nature and composition of this industrial sector including the characteristics of major hazards are summarized. The present situation with regard to a number of key technical aspects involved in the use of safety and reliability approaches in the power sector is discussed. Based on this review a Technology Maturity Matrix is synthesized. Barriers to the wider use of risk and reliability methods in the design and operation of power installations are identified and possible ways of overcoming these barriers are suggested. Key issues and priorities for research are identified.

This chapter discusses the use of risk analysis to support decision making on maintenance activities. In recent years there has been a growing interest in the use of risk analysis and risk based (informed) approaches for guiding decisions on maintenance, see, e.g., Vatn et al. (1996), Clarotti et al. (1997), Dekker (1996) and Cepin (2002), and this topic has also been given much attention in industry see for example van Manen et. al. (1997), Knoll et al. (1996), Perryman et al. (1995) and Podofillini et al. (2006). This chapter provides a critical review of some of the key building blocks of the theories and methods developed. We also discuss some critical factors for ensuring a successful use of risk analysis for maintenance applications. The issues discussed include: Risk descriptions and categorisations Uncertainty assessments Risk acceptance and risk informed decision making Selection of appropriate methods and tools An example is presented of a detailed risk analysis, showing the effect of maintenance efforts on risk.

This chapter describes the relevant steps for the design of a preventive maintenance program in an oil refinery plant and its application. The method was developed during a period of 3 years in one of the main Italian refinery.

There are many models in the literature that have been proposed in the last decades aimed at assessing the reliability, availability and maintainability (RAM) of safety equipment, many of them with a focus on their use to assess the risk level of a technological system or to search for appropriate design and/or surveillance and maintenance policies in order to assure that an optimum level of RAM of safety systems is kept during all the plant operational life. This paper proposes a new approach for RAM modelling that accounts for equipment ageing and maintenance and testing effectiveness of equipment consisting of multiple items in an integrated manner. This model is then used to perform the simultaneous optimization of testing and maintenance for ageing equipment consisting of multiple items. An example of application is provided, which considers a simplified High Pressure Injection System (HPIS) of a typical Power Water Reactor (PWR). Basically, this system consists of motor driven pumps (MDP) and motor operated valves (MOV), where both types of components consists of two items each. These components present different failure and cause modes and behaviours, and they also undertake complex test and maintenance activities depending on the item involved. The results of the example of application demonstrate that the optimization algorithm provide the best solutions when the optimization problem is formulated and solved considering full flexibility in the implementation of testing and maintenance activities taking part of such an integrated RAM model.

The term common cause failure is related to a fact that several components can fail or become unavailable due to a particular cause of failure and a coupling mechanism that creates the condition for multiple components to be affected by the same cause. The objective of this paper is to investigate the feasibility of common cause analysis to an electric power system, its applicability, the most appropriate method and the effects of the results to the reliability of the power system, where the static conditions are assumed. The method for assessment of reliability of power systems is developed, which is based on the fault tree analysis and which integrates the common cause analysis. The results of example studies shows that the method is suitable for reliability analysis of power systems and for improvement of power systems such as their upgrade in optimized manner and for optimization of their maintenance with minimizing the costs.

The systems used in production, transportation services, and communication services constitute the majority part of not only industrial activities but also our daily life. Most of them have many units or components with various structure that will degrade with time or usage, and even suffer from a sudden failure due to the random shocks. For some systems, such as military systems, aircrafts, and nuclear power plants, they are of great importance and cannot afford to any failure. A machine in industrial plant failing to work properly will interpret the whole production assembly line and cost a large amount of capital and labor, while the failure of the aircraft will endanger the life of all the passengers. Therefore, maintenance on these systems is necessary due to the two aspects: (1) prolong the service life of the products; (2) improve the system reliability to avoid unnecessary failure.

The simultaneous optimization of Test Intervals and Test Strategies considered in the Surveillance Requirements for Nuclear Power Plants is a multiple-objective scheduling problem which has been tackled only recently [7-8]. The formulation of this class of problems presents dependence between the current values of some variables and the allowed upper values of others, defining a domain composed by a set of disjoint subsets. In this paper we propose and study different alternatives for representing and solve the problem using multiple-objective genetic algorithms. Additionally, the concepts of ε-dominance and εapproximate Pareto set were employed to post-process and compare the outcomes. As a result, the introduced percentage representation showed the best performance.

The significant impact that common cause failures can have on reliability and safety of a system comprising redundant components is widely acknowledged. These common cause failures can significantly endanger the benefits of the redundancy which is seen as the main principle upon which safety systems design is based. Thus, consideration of common cause failures is one of the most challenging and critical issues in the probabilistic safety assessment (PSA). This is especially emphasized within PSA fault tree modeling of safety systems within nuclear power plants. This study presents a new method for explicit modeling of single component failure in different common cause component groups (CCCGs) as well as the associated limitations. These limitations arise from the principle of explicit modeling of common cause failures where each components failure space can be decomposed, in terms of causes, to independent portion and dependent, common cause failure affected portion. A method-complementary approach for acting upon these limitations, presented in terms of constraining the dependent portion of the total components failure space, is proposed within the paper. The method is applied on a selected case study system. The results and insights gained out of this application are presented and discussed. In general, the application of this method implicates improved and more detailed PSA models. These improved models consequently direct more realistic PSA results.

The power system reliability analysis method is developed from the aspect of reliable delivery of electrical energy to customers. The method is developed based on the fault tree analysis, which is widely applied in the Probabilistic Safety Assessment (PSA). The method is adapted for the power system reliability analysis. The method is developed in a way that only the basic reliability parameters of the analysed power system are necessary as an input for the calculation of reliability indices of the system. The modeling and analysis was performed on an example power system consisting of eight substations. The results include the level of reliability of current power system configuration, the combinations of component failures resulting in a failed power delivery to loads, and the importance factors for components and subsystems.

The nuclear power plants risk reduction by application of the probabilistic safety assessment is one of the main focuses of the nuclear safety today. The goal is the reduction of the unavailability of the nuclear power plants safety systems. On the other hand, the world NPP fleet is ageing fast. Equipment ageing has gradually become a major concern in the nuclear industry since the number of safety systems components, that are approaching their wear-out stage, is rising fast. A previously developed model for assessing time-dependent unavailability of ageing safety equipment is briefly presented and discussed herein. One of the essential features of the model is that it simultaneously considers the effects of performing surveillance testing as well as preventive maintenance, corrective maintenance and overhaul. Also, the ageing-implicated adverse effects on component availability are being explicitly considered as an integral part of this time-dependent unavailability model. Subsequently, the model can be coupled to commercial software for systemand plant-level modelling. This paper is aimed towards performing sensitivity analysis of the developed model. A component level resolution is selected as the basis for performing the analysis. Investigation of the influence of different component-relevant input parameters on the calculated equipment unavailability is the goal of the analysis. The dependency of the calculated component unavailability on the corresponding surveillance test interval is of a particular interest. The results are presented and discussed.

The maintenance of equipment has been an important issue for companies for many years. For systems with hidden or unrevealed failures (i.e., failures are not self-announcing), a common practice is to regularly inspect the system looking for such failures. Examples of these systems include protective devices, emergency devices, standby units, underwater devices etc. If no periodical inspection is scheduled, and a hidden failure has already occurred, severe consequences may result. Research on periodical inspection seeks to establish the optimal inspection interval (Failure Finding Interval) of systems to maximize availability and/or minimize expected cost. Research also focuses on important system parameters such as unavailability. Most research in this area considers non-negligible downtime due to repair/replacement but ignores the downtime caused by inspections. In many situations, however, inspection time is non-negligible. We address this gap by proposing an optimal failure finding interval (FFI) considering both non-negligible inspection time and repair/replacement time. A novel feature of this work is the development of models for both age-based and calendar-based inspection policies with