A study on multi-level redundancy allocation in coherent systems formed by modules

The present work studies the effect of redundancies on the reliability of coherent systems formed by modules. Different redundancies at components’ level versus redundancies at modules’ level are investigated, including active and standby redundancies. For that, a new model is presented. This model takes into account the dependence among the components, as well as, the dependence among the modules of the system. In both cases, the dependence structure is modeled by copula functions. Several results are provided to compare systems consisting of heterogeneous components. The comparisons are distribution-free with respect to the components. In particular, we consider the cases when the components in the modules are independent and connected (or not) in series, and when the components are dependent within the modules. In both cases, it is assumed that the modules can be dependent. Furthermore, the case in which the components in each module are identically distributed (dependent or independent) is also considered. We illustrate the theoretical results with several examplesNT is partially supported by Ministerio de Ciencia e Innovación of Spain under grant PID2019-108079GB-C22/AEI/10.13039/501100011033. AA was supported by Ministerio de Economía y Competitividad of Spain under grant MTM2017-89577-P. Finally, JN is partially supported by Ministerio de Ciencia e Innovación of Spain under grant PID2019-103971GBI00/ AEI/10.13039/50110001103

Optimization of systems reliability by metaheuristic approach

The application of metaheuristic approaches in addressing the reliability of systems through optimization is of greater interest to researchers and designers in recent years. Reliability optimization has become an essential part of the design and operation of largescale manufacturing systems. This thesis addresses the optimization of system-reliability for series–parallel systems to solve redundant, continuous, and combinatorial optimization problems in reliability engineering by using metaheuristic approaches (MAs). The problem is to select the best redundancy strategy, component, and redundancy level for each subsystem to maximize the system reliability under system-level constraints. This type of problem involves the selection of components with multiple choices and redundancy levels that yield the maximum benefits, and it is subject to the cost and weight constraints at the system level. These are very common and realistic problems faced in the conceptual design of numerous engineering systems. The development of efficient solutions to these problems is becoming progressively important because mechanical systems are becoming increasingly complex, while development plans are decreasing in size and reliability requirements are rapidly changing and becoming increasingly difficult to adhere to. An optimal design solution can be obtained very frequently and more quickly by using genetic algorithm redundancy allocation problems (GARAPs). In general, redundancy allocation problems (RAPs) are difficult to solve for real cases, especially in large-scale situations. In this study, the reliability optimization of a series–parallel by using a genetic algorithm (GA) and statistical analysis is considered. The approach discussed herein can be applied to address the challenges in system reliability that includes redundant numbers of carefully chosen modules, overall cost, and overall weight. Most related studies have focused only on the single-objective optimization of RAP. Multiobjective optimization has not yet attracted much attention. This research project examines the multiobjective situation by focusing on multiobjective formulation, which is useful in maximizing system reliability while simultaneously minimizing system cost and weight to solve the RAP. The present study applies a methodology for optimizing the reliability of a series–parallel system based on multiobjective optimization and multistate reliability by using a hybrid GA and a fuzzy function. The study aims to determine the strategy for selecting the degree of redundancy for every subsystem to exploit the general system reliability depending on the overall cost and weight limitations. In addition, the outcomes of the case study for optimizing the reliability of the series–parallel system are presented, and the relationships with previously investigated phenomena are presented to determine the performance of the GA under review. Furthermore, this study established a new metaheuristic-based technique for resolving multiobjective optimization challenges, such as the common reliability redundancy allocation problem. Additionally, a new simulation process was developed to generate practical tools for designing reliable series–parallel systems. Hence, metaheuristic methods were applied for solving such difficult and complex problems. In addition, metaheuristics provide a useful compromise between the amount of computation time required and the quality of the approximated solution space. The industrial challenges include the maximization of system reliability subject to limited system cost and weight, minimization of system weight subject to limited system cost and the system reliability requirements and increasing of quality components through optimization and system reliability. Furthermore, a real-life situation research on security control of a gas turbine in the overspeed state was explored in this study with the aim of verifying the proposed algorithm from the context of system optimization

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Reliability, Availability and Maintainability (RAM) Analysis for Offshore High Pressure Compressor

Reliability, Availability and Maintainability (RAM) helps in optimizing performance of equipment. The availability can be improved by the enhancement of the reliability and maintainability. Equipment failure in offshore facilities are difficult to be predicted hence sudden failure of an equipment lead to reduction in output, loss of production and high maintenance cost due to unplanned maintenance. This study examined and analysed the failure mode of high pressure compressor at offshore platform in order to identify its critical failure mode. Failure and repair data are utilized to determine reliability and maintainability of the high pressure compressor. Reliability and maintainability analysis was carried out with the aid of Reliasoft Weibull++ software to obtain the required parameters while ReliaSoft BlockSim software was used for reliability block diagram (RBD) construction and simulation to obtain the availability of the high pressure compressor. The developed model can improve the performance of the high pressure compressor since it is validated with the actual model. From this RAM analysis, the overall performance of high pressure compressor can be increase by conducting Root Cause Failure Analysis (RCFA) which focusing on the most critical failure mode. The optimization of maintenance schedule can lead to the reduction of maintenance cost

Contributions au développement de politiques de remplacement préventif pour des sytèmes multi-composants

Dans cette thèse, nous proposons de développer des politiques de remplacement préventif pour des systèmes multi-composants. Ces systèmes sont composés de plusieurs composants selon une configuration bien déterminée et dont l’état se dégrade d’une manière aléatoire. Les politiques de remplacement définissent les actions à entreprendre en fonction de l'état du système ou de ses composants et ont pour objectif de retarder l'apparition des pannes et de prolonger la durée de vie du système. Sur le plan théorique, la généralisation des modèles de remplacement des systèmes mono-composants à des systèmes multi-composants n'est pas évidente. La difficulté réside essentiellement dans l’existence d’interaction ou de dépendance entre les différents composants du système. Nous nous sommes concentrés dans cette thèse sur les dépendances stochastique et économique entre les composants. Pour la dépendance stochastique, la propagation de la panne a été modélisée par l’effet domino pour un système parallèle à deux composants. Nous avons proposé deux politiques de remplacement de type Age. Dans la première politique, nous avons supposé que la structure des coûts est constante alors que dans la deuxième politique cette hypothèse a été modifiée en prenant une structure de coûts variable. Nous avons aussi proposé dans le cadre de la dépendance stochastique un modèle de remplacement bi-objectif qui optimise à la fois le coût espéré du remplacement et la disponibilité du système. Pour la dépendance économique, nous avons proposé une politique de remplacement basée sur le comptage des pannes pour un système parallèle et nous l’avons intégrée dans un modèle d’allocation de la redondance d’un système série-parallèle. Le modèle mathématique a été résolu par une approche heuristique basée sur l’algorithme du recuit simulé.The aim of this thesis is to develop preventive replacement policies for multi-component systems. Systems are composed of several components connected under a known configuration and subject to random failures. Each replacement policy defines the actions to be taken according to the state of the system or its components and it is intended to delay the occurrence of failures and extend the lifetime of the system. From the theoretical point of view, the extension of replacement models from single-component systems to multi-component systems is not obvious. The difficulty is due primarily to the interaction or dependence between the different components of the system. In this thesis the focus has been put on the stochastic and economic dependencies between components. For stochastic dependence the propagation of the failure is modeled by the domino effect for a two-component parallel system, and two age replacement policies are investigated. In the first policy, we assumed that the cost structure is constant whereas in the second policy a variable cost structure is assumed. We proposed also a bi-objective replacement model that optimizes both expected replacement cost rate and system availability. For economic dependence, we proposed a failure counting replacement policy for a parallel system and we integrated it in a redundancy allocation model for a serie-parallel system. The mathematical model has been built taking account of this policy and Simulated Annealing algorithm has been used as resolution approach

Nonparametric Predictive Inference for System Reliability

This thesis provides a new method for statistical inference on system reliability on the basis of limited information resulting from component testing. This method is called Nonparametric Predictive Inference (NPI). We present NPI for system reliability, in particular NPI for k-out-of-m systems, and for systems that consist of multiple ki-out-of-mi subsystems in series configuration. The algorithm for optimal redundancy allocation, with additional components added to subsystems one at a time is presented. We also illustrate redundancy allocation for the same system in case the costs of additional components differ per subsystem. Then NPI is presented for system reliability in a similar setting, but with all subsystems consisting of the same single type of component. As a further step in the development of NPI for system reliability, where more general system structures can be considered, nonparametric predictive inference for reliability of voting systems with multiple component types is presented. We start with a single voting system with multiple component types, then we extend to a series configuration of voting subsystems with multiple component types. Throughout this thesis we assume information from tests of nt components of type t

Reliability optimization of hardware components and system´s topology during early design phase

To master the complexity in modern vehicle, Original Equipment Manufactures (OEM) attempt to integrate as many functions as possible into the given Electronic Control Unit (ECU), sensors, and actuators without degrading the safety and comfort functionalities. Furthermore scalability, versatility, and performance of products are key to success of electronic development in new modern vehicles. Various functional and nonfunctional requirements obviously shall be fulfilled during development of such complex systems. Choosing of hardware design structure and determination of hardware characteristics are the initial steps during early design phase. The conventional methods for selection of hardware components and topologies are mostly functional-driven. Conventional approaches are largely lacking in versatility and scalability. Due to innovative and complex trend of mechatronic product development, new approaches for hardware decision must be available which support the designers in case of changing (growing) customer demands. One of most important customer requirement for a complex system is reliability. The need for more reliable system design drives up the cost of design and influences the other system characteristics such as weight, power consumption, size, etc. These design goals like reliability, cost potentially impose conflicting requirements on the technical and economic performance of a system design. Hence, visualization and evaluating of the conflicting design preferences and early choosing optimal design are one of the most critical issues during design stage. Many multi-objective optimization approaches have been proposed to tackle this challenge. This dissertation proposes an efficient reliability optimization framework which aids the designers to determine the optimal hardware topology with optimal set of components under known technical and financial restrictions. The proposed reliability optimization framework allows describing the hardware structure of a complex system by a System Reliability Matrix (SRM) and the failure rate vector of involving hardware components. The reliability characteristics of components and the redundancy policy can be varied automatically via the SRM and its corresponding failure rate vector in order to determine optimal solutions. The proposed methodology ultimately addresses the most efficient system architecture (topology) and ascertain the unknown reliability characteristics of hardware components under consideration of financial and technical constraints. It is to be noted that the numerical deterministic search methods and genetic algorithms are applied to optimize the defined objective function under multiple constraints (reliability, cost, weight, size, etc.) and to determine the reliability characteristics of components. A general enumerative algorithm generates all design architectures (topologies) and filters the feasible design architectures (topologies) based on given constraints like budget and etc

Photoheliograph study for the Apollo telescope mount

Photoheliograph study for Apollo telescope moun

Reliability, Availability and Maintainability (RAM) Analysis for Offshore High Pressure Compressor

Reliability, Availability and Maintainability (RAM) helps in optimizing performance of equipment. The availability can be improved by the enhancement of the reliability and maintainability. Equipment failure in offshore facilities are difficult to be predicted hence sudden failure of an equipment lead to reduction in output, loss of production and high maintenance cost due to unplanned maintenance. This study examined and analysed the failure mode of high pressure compressor at offshore platform in order to identify its critical failure mode. Failure and repair data are utilized to determine reliability and maintainability of the high pressure compressor. Reliability and maintainability analysis was carried out with the aid of Reliasoft Weibull++ software to obtain the required parameters while ReliaSoft BlockSim software was used for reliability block diagram (RBD) construction and simulation to obtain the availability of the high pressure compressor. The developed model can improve the performance of the high pressure compressor since it is validated with the actual model. From this RAM analysis, the overall performance of high pressure compressor can be increase by conducting Root Cause Failure Analysis (RCFA) which focusing on the most critical failure mode. The optimization of maintenance schedule can lead to the reduction of maintenance cost