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

Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness

[EN] 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.Authors are grateful to the Spanish Ministry of Science and Innovation for the financial support of this work (research project ENE2013-45540-R) and the Doctoral fellow (BES-2011-043906 and BES-2014-067602).Martón Lluch, I.; Martorell Aigües, P.; Mullor, R.; Sánchez Galdón, AI.; Martorell Alsina, SS. (2016). Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness. Reliability Engineering and System Safety. 153:151-158. https://doi.org/10.1016/j.ress.2016.04.015S15115815

Enhancement in Reliability for Multi-core system consisting of One Instruction Cores

Rapid CMOS device size reduction resulted in billions of transistors on a chip have led to integration of many cores leading to many challenges such as increased power dissipation, thermal dissipation, occurrence of transient faults and permanent faults. The mitigation of transient faults and permanent faults at the core level has become an important design parameter in a multi-core scenario. Core level techniques is a redundancy-based fault mitigation technique that improves the lifetime reliability of multi-core systems. In an asymmetric multi-core system, the smaller cores provide fault tolerance to larger cores is a core level fault mitigation technique that has gained momentum and focus from many researchers. The paper presents an economical, asymmetric multi-core system with one instruction cores (MCSOIC). The term Hardware Cost Estimation signifies power and area estimation for MCS-OIC. In MCSOIC, OIC is a warm standby redundant core. OICs provide functional support to conventional cores for shorter periods of time. To evaluate the idea, different configurations of MCSOIC is synthesized using FPGA and ASIC. The maximum power overhead and maximum area overhead are 0.46% and 11.4% respectively. The behavior of OICs in MCS-OIC is modelled using a One-Shot System (OSS) model for reliability analysis. The model parameters namely, readiness, wakeup probability and start-up-strategy for OSS are mapped to the multi-core systems with OICs. Expressions for system reliability is derived. System reliability is estimated for special cases.Comment: 46 page

Optimal Allocation of Resources in Reliability Growth

Reliability growth testing seeks to identify and remove failure modes in order to improve system reliability. This dissertation centers around the resource allocation across the components of a multi-component system to maximize system reliability. We summarize this dissertation’s contributions to optimal resource allocation in reliability growth. Chapter 2 seeks to deploy limited testing resources across the components of a series-parallel system in effort to maximize system reliability under the assumption that each component’s reliability exhibits growth according to an AMSAA model with known parameters. An optimization model for this problem is developed and then extended to consider the allocation of testing resources in a series-parallel system with consideration for the possibility of testing at different levels (system, subsystem, and component). We contribute a class of exact algorithms that decomposes the problem based upon the series-parallel structure. We prove the algorithm is finite, compare it with heuristic approaches on a set of test instances, and provide detailed analyses of numerical examples. In Chapter 3, we extend model in Chapter 2 to solve a robust optimization version of this problem in which AMSAA parameters are uncertain but assumed to lie within a budget-restricted uncertainty set. We model the problem of robust allocation of testing resources to maximize system reliability for both series and series-parallel systems, and we develop and analyze exact solution approaches for this problem based on a cutting plane algorithm. Computational results demonstrate the value of the robust optimization approach as compared to deterministic alternatives. In the last chapter, we develop a new model that merges testing components and installing redundancies within an integrated optimization model that maximizes system reliability. Specifically, our model considers a series-parallel system in which the system reliability can be improved by both testing components and installing redundant components. We contribute an exact algorithm that decomposes the problem into smaller integer linear programs. We prove that this algorithm is finite and apply it to a set of instances. Experiments demonstrate that the integrated approach generates greater reliabilities than applying test planning and redundancy allocation models iteratively, and moreover, it yields significant savings in computational time

Mars spacecraft power system development Interim report

Modified Mariner power system design for Mars mission

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case