Estimation of component redundancy in optimal age maintenance

The classical Optimal Age-Replacement defines the maintenance strategy based on the equipment failure consequences. For severe consequences an early equipment replacement is recommended. For minor consequences the repair after failure is proposed. One way of reducing the failure consequences is the use of redundancies, especially if the equipment failure rate is decreasing over time, since in this case the preventive replacement does not reduce the risk of failure. The estimation of an active component redundancy degree is very important in order to minimize the life-cycle cost. If it is possible to make these estimations in the early phase of system design, the implementation is easier and the amortization faster. This work proposes an adaptation of the Optimal Age-Replacement method in order to simultaneously optimize the equipment redundancy allocation and the maintenance plan. The main goal is to provide a simple methodology, requiring the fewer data possible. A set of examples are presented illustrating that this methodology covers a wide variety of operating conditions. The optimization of the number of repairs between each replacement, in the cases of imperfect repairs, is another feature of this methodology

Integrated Optimization of IT Service Performance and Availability Using Performability Prediction Models

Optimizing the performance and availability of an IT service in the design stage are typically considered as independent tasks. However, since both aspects are related to one another, these activities could be combined by applying performability models, in which both the performance and the availability of a service can be more accurately predicted. In this paper, a design optimization problem for IT services is defined and applied in two scenarios, one of which considers a mechanism in which redundant components can be used both for failover as well as handling overload situations. Results show that including such aspects affecting both availability and performance in prediction models can lead to more cost-effective service designs. Thus, performability prediction models are one opportunity to combine performance and availability management for IT services

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

Multi-objective optimisation of safety-critical hierarchical systems

Achieving high reliability, particularly in safety critical systems, is an important and often mandatory requirement. At the same time costs should be kept as low as possible. Finding an optimum balance between maximising a system's reliability and minimising its cost is a hard combinatorial problem. As the size and complexity of a system increases, so does the scale of the problem faced by the designers. To address these difficulties, meta-heuristics such as Genetic Algorithms and Tabu Search algorithms have been applied in the past for automatically determining the optimal allocation of redundancies in a system as a mechanism for optimising the reliability and cost characteristics of that system. In all cases, simple reliability block diagrams with restrictive assumptions, such as failure independence and limited 2-state failure modes, were used for evaluating the reliability of the candidate designs produced by the various algorithms.This thesis argues that a departure from this restrictive evaluation model is possible by using a new model-based reliability evaluation technique called Hierachically Performed Hazard Origin and Propagation Studies (HiP-HOPS). HiP-HOPS can overcome the limitations imposed by reliability block diagrams by providing automatic analysis of complex engineering models with multiple failure modes. The thesis demonstrates that, used as the fitness evaluating component of a multi-objective Genetic Algorithm, HiP-HOPS can be used to solve the problem of redundancy allocation effectively and with relative efficiency. Furthermore, the ability of HiP-HOPS to model and automatically analyse complex engineering models, with multiple failure modes, allows the Genetic Algorithm to potentially optimise systems using more flexible strategies, not just series-parallel. The results of this thesis show the feasibility of the approach and point to a number of directions for future work to consider

Novel models and algorithms for systems reliability modeling and optimization

Recent growth in the scale and complexity of products and technologies in the defense and other industries is challenging product development, realization, and sustainment costs. Uncontrolled costs and routine budget overruns are causing all parties involved to seek lean product development processes and treatment of reliability, availability, and maintainability of the system as a true design parameter . To this effect, accurate estimation and management of the system reliability of a design during the earliest stages of new product development is not only critical for managing product development and manufacturing costs but also to control life cycle costs (LCC). In this regard, the overall objective of this research study is to develop an integrated framework for design for reliability (DFR) during upfront product development by treating reliability as a design parameter. The aim here is to develop the theory, methods, and tools necessary for: 1) accurate assessment of system reliability and availability and 2) optimization of the design to meet system reliability targets. In modeling the system reliability and availability, we aim to address the limitations of existing methods, in particular the Markov chains method and the Dynamic Bayesian Network approach, by incorporating a Continuous Time Bayesian Network framework for more effective modeling of sub-system/component interactions, dependencies, and various repair policies. We also propose a multi-object optimization scheme to aid the designer in obtaining optimal design(s) with respect to system reliability/availability targets and other system design requirements. In particular, the optimization scheme would entail optimal selection of sub-system and component alternatives. The theory, methods, and tools to be developed will be extensively tested and validated using simulation test-bed data and actual case studies from our industry partners

Advanced flight control system study

The architecture, requirements, and system elements of an ultrareliable, advanced flight control system are described. The basic criteria are functional reliability of 10 to the minus 10 power/hour of flight and only 6 month scheduled maintenance. A distributed system architecture is described, including a multiplexed communication system, reliable bus controller, the use of skewed sensor arrays, and actuator interfaces. Test bed and flight evaluation program are proposed

Integrating model checking with HiP-HOPS in model-based safety analysis

The ability to perform an effective and robust safety analysis on the design of modern safety–critical systems is crucial. Model-based safety analysis (MBSA) has been introduced in recent years to support the assessment of complex system design by focusing on the system model as the central artefact, and by automating the synthesis and analysis of failure-extended models. Model checking and failure logic synthesis and analysis (FLSA) are two prominent MBSA paradigms. Extensive research has placed emphasis on the development of these techniques, but discussion on their integration remains limited. In this paper, we propose a technique in which model checking and Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) – an advanced FLSA technique – can be applied synergistically with benefit for the MBSA process. The application of the technique is illustrated through an example of a brake-by-wire system

Engineering failure analysis and design optimisation with HiP-HOPS

The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved