Dynamic behavioural model for assessing impact of regeneration actions on system availability: Application to weapon systems

Mastering system availability all along the system life cycle is now a critical issue with regards to systems engineering. It is more true for military systems which operate in a battle context. Indeed as they must act in a hostile environment, they can become unavailable due to failures of or damage to the system. In both cases, system regeneration is required to restore its availability. Many approaches based on system modelling have been developed to assess availability. However, very few of them take battlefield damage into account and relevant methods for the model development are missing. In this paper, a modelling method for architecture of weapon system of systems that supports regeneration engineering is proposed. On the one hand, this method relies on a unified failure/damage approach to extend acknowledged availability models. It allows to integrate failures, damages, as well as the possibility of regeneration, into operational availability assessment. Architectures are modelled as a set of operational functions, supported by components that belong to platform (system). Modelling atoms (i.e. elementary units of modelling) for both the architecture components and functions are defined, based on state-space formalism. Monte Carlo method is used to estimate availability through simulation. Availability of the architecture is defined on the basis of the possible states of the required functions for a mission. The states of a function directly depend on the state of the corresponding components (i.e. the components that support the function). Aggregation rules define the state of the function knowing the states of each component. Aggregation is defined by means of combinatorial equations of the component states. The modelling approach is supported by means of stochastic activity network for the models simulation. Results are analysed in terms of graphs of availability for mission's days. Thus, given the simulation results, it is possible to plan combat missions based on criteria such as the number of platforms to be involved given functions required for the mission or the mean of regeneration to be deployed given the possible threats. Further, the simulation will help towards the design of improved architecture of system of systems which could focus on the factors affecting the availability.     

A reliability allocation method of mechanism considering system performance reliability

It is generally believed that the reliability of a mechanical system is determined by its composition. The system operates properly when all of its components do not fail. Based on this assumption, the reliability of the system can be represented by the reliability of its components. A problem arises when applying this hypothesis to a system containing motion mechanisms. There is a phenomenon in motion mechanism that the components do not happen structural failure (we call it “Type I failure”) and joint failure (we call it “Type II failure”), but the function of the mechanism cannot meet the requirements (we call it “Type III failure”). A reliability allocation method, which synthetically considers the composition and Type III failure modes of the motion mechanism, is proposed to solve this problem. A relative dispersion factor is introduced to describe the failure dependence of components and is proposed to calculate the complexity and criticality. A series system reliability allocation model considering three types of failure modes is established. Finally, using an airplane gear door lock mechanism as an example, a comparative analysis of the system reliability allocation results with and without considering Type III failure modes is made. The allocation results show the component reliability value without considering Type III failure modes is less than that when considering them, which will increase the system hazards. The result considering Type III failure modes is more reasonable than that from the traditional method.     

Component choice for managing risk in engineered systems with generalized risk/cost functions

The constrained optimization of resource allocation to minimize the probability of failure of an engineered system relies on a probabilistic risk analysis of that system, and on ‘risk/cost functions’. These functions describe, for each possible improvement of the system's robustness, the corresponding gain of reliability given the considered component or management factor to be upgraded. These improvements can include, for example, the choice of components of different robustness levels (at different costs), addition of redundancies, or changes in operating and maintenance procedures. The optimization model is generally constrained by a maximum budget, a schedule deadline, or a maximum number of qualified personnel. A key question is thus the nature of the risk/cost function linking the costs involved and the corresponding failure-risk reduction. Most of the methods proposed in the past have relied on continuous, convex risk/cost functions reflecting decreasing marginal returns. In reality, the risk/cost functions can be simple step functions (e.g. a discrete choice among possible components), discontinuous functions characterized by continuous segments between points of discontinuity (e.g. a discrete choice among components that can be of continuously increasing levels of robustness), or continuous functions (e.g. exponentially decreasing failure risk with added resources).This paper describes a general method for the optimization of the robustness of a complex engineered system in which all three risk/cost function types may be relevant. We illustrate the method with a satellite design problem. We conclude with a discussion of the complexity of the resolution of this general type of optimization problem given the number and the types of variables involved.     

Mechanical system reliability analysis using a combination of graph theory and Boolean function

A new method based on graph theory and Boolean function for assessing reliability of mechanical systems is proposed. The procedure for this approach consists of two parts. By using the graph theory, the formula for the reliability of a mechanical system that considers the interrelations of subsystems or components is generated. Use of the Boolean function to examine the failure interactions of two particular elements of the system, followed with demonstrations of how to incorporate such failure dependencies into the analysis of larger systems, a constructive algorithm for quantifying the genuine interconnections between the subsystems or components is provided. The combination of graph theory and Boolean function provides an effective way to evaluate the reliability of a large, complex mechanical system. A numerical example demonstrates that this method an effective approaches in system reliability analysis.     

Nonlinear formation of holographic images of obscurations in laser beams

Computer models are used to simulate the nonlinear formation of images of obscurations in laser beams. The predictions of the model are found to be in good agreement with measurements conducted in the nonlinear regime corresponding to a typical solid-state laser operation. In this regime, peak-to-mean fluence ratios large enough to induce damage in optical components are observed. The amplitude of the images and their location along the propagation axis are accurately predicted by the simulations. This indicates that the model is a reliable design tool for specifying component staging and optical specifications to avoid optical damage by this mechanism.     

基于Copula函数的桥梁系统地震易损性方法研究

桥梁系统地震易损性分析的关键是建立桥墩、支座等多个构件的联合概率分布函数。然而,由于构件地震需求之间的相关性,直接建立构件之间的联合概率分布函数较为困难。为此,引入Copula函数方法,将构件地震需求之间的相关性和各构件的边缘概率分布函数进行分离,从而简化了联合分布函数的建模过程。在桥墩、支座地震易损性的基础上,基于Copula联合概率分布函数,建立了桥梁系统的易损性曲线,并将其和一阶界限法及Monte Carlo方法的分析结果进行对比。结果表明:基于Copula函数得到的桥梁系统易损性在整个地震动强度范围内均位于一阶界限法的上、下界之间;和Monte Carlo方法相比,Copula函数方法不仅考虑了构件地震需求之间的非线性相关关系,而且避免了大量的数值抽样,使计算效率显著提高。     

Genetic algorithm attributes for component selection

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison of these attributes is performed using two industrial design problems. A set of genetic algorithm attributes including integer coding, uniform crossover, anti-incest mating, variable mating and mutation rates, retention of population members from generation to generation, and an attention shifted penalty function are suggested for a more efficient search in component selection problems.     

On the analysis of field failure data for repairable systems

Reliability assessments of repairable (electronic) equipment are often based on failure data recorded under field conditions. The main objective in the analyses is to provide information that can be used in improving the reliability through design changes. For this purpose it is of particular interest to be able to locate ‘trouble-makers’, i.e. components that are particular likely to fail. In the present context, reliability is measured in terms of the mean cumulative number of failures as a function of time. This function may be considered for the system as a whole, or for stratified data. The stratification is obtained by sorting data according to different factors, such as component positions, production series, etc. The mean cumulative number of failures can then be estimated either nonparametrically as an average of the observed failures, or parametrically, if a certain model for the lifetimes of the components involved is assumed. As an example we here consider a simple component lifetime model based on the assumption that components are ‘drawn’ randomly from a heterogeneous population, where a small proportion of the components are weak (with a small mean lifetime), and the remaining are standard components (with a large mean lifetime). This model enables formulation of an analytical expression for the mean cumulative number of failures. In both the nonparametric and the parametric case the uncertainty of the estimation may be assessed by computing a confidence interval for the estimated values (a confidence band for the estimated time functions). The determination of confidence bands provides a basis for assessing the significance of the factors underlying the stratification. The methods are illustrated through an industrial case study using field failure data.     

A multi-objective discrete reliability optimization problem for dissimilar-unit standby systems

A new methodology for the reliability optimization of a k dissimilar-unit nonrepairable cold-standby redundant system is introduced in this paper. Each unit is composed of a number of independent components with generalized Erlang distributions of lifetimes arranged in a series–parallel configuration. We also propose an approximate technique to extend the model to the general types of nonconstant hazard functions. To evaluate the system reliability, we apply the shortest path technique in stochastic networks. The purchase cost of each component is assumed to be an increasing function of its expected lifetime. There are multiple component choices with different distribution parameters available for replacement with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system to minimize the initial purchase cost of the system, maximize the system mean time to failure, minimize the system variance of time to failure, and also maximize the system reliability at the mission time. The goal attainment method is used to solve a discrete time approximation of the original problem.      

Automatic process plan generation in an operative process planning system

The architecture and design of process planning systems should follow the architecture and design of the manufacturing systems they are intended to serve. This article describes a process planning system specifically intended for a workshop orientated factory, a factory which is specialized for the production of a limited number of group technological product lines. The goal of the system is to generate automatically and rapidly reliable part programs from order information. With the system, a process plan can be generated by workshop personnel on the basis of process plan specifications stored in part family models. A part program generation algorithm can then create a part program automatically by pasting together segments of program code stored in the process models of the system. The part program generator can be tailored by new parametric part program definitions related to features. The tool paths of the generated code can be visualised. The system described forms the operative process planner part of the MCOES (BE-3528) process planning system.     

Filter allocation and replacement strategies in fluid power system under uncertainty: a fuzzy robust nonlinear programming approach

A fuzzy robust nonlinear programming model is developed for the assessment of filter allocation and replacement strategies in hydraulic systems under uncertainty. It integrates the methods of fuzzy mathematic programming (FMP) and robust programming (RP) within the mixed integer nonlinear programming framework, and can facilitate dynamic analysis and optimization of filters allocation and replacement planning where the uncertainties are expressed as fuzzy membership functions. In modeling formulation, theory of contamination wear of typical hydraulic components is introduced to strengthen the presentation of relationship between system contamination and work performance. The fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The piecewise linearization approach is employed to handle the nonlinearities of problem. The developed method has been applied to a case of planning filter allocation and replacement strategies under uncertainty and the generated optimal solution will help to reduce the total system cost and failure-risk level of the FPS.     

Component-based modeling of systems for automated fault tree generation

One of the challenges in the field of automated fault tree construction is to find an efficient modeling approach that can support modeling of different types of systems without ignoring any necessary details. In this paper, we are going to represent a new system of modeling approach for computer-aided fault tree generation. In this method, every system model is composed of some components and different types of flows propagating through them. Each component has a function table that describes its input-output relations. For the components having different operational states, there is also a state transition table. Each component can communicate with other components in the system only through its inputs and outputs. A trace-back algorithm is proposed that can be applied to the system model to generate the required fault trees. The system modeling approach and the fault tree construction algorithm are applied to a fire sprinkler system and the results are presented.     

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slitting method require an inverse analysis to solve the problem. The accuracy of the result as well as an uncertainty component (the model uncertainty) depends on the basis functions used in the inverse solution. The use of a series expansion as the basis functions for the inverse solution was analysed in a previous work for the particular case where functions orders grew consecutively. The present work presents a new estimation of the model uncertainty and a new improved methodology to select the final basis functions for the case where the basis is composed of polynomials. Including nonconsecutive polynomial orders in the basis generates a larger space of possible solutions to be evaluated and allows the possibility to include higher-order polynomials. The paper includes a comparison with two other inverse analyses methodologies applied to synthetically generated data. With the new methodology, the final error is reduced and the uncertainty estimation improved.     

A framework for capturing and analyzing the failures due to system/component interactions

To keep up with the speed of globalization and growing customer demands for more technology‐oriented products, modern systems are becoming increasingly more complex. This complexity gives rise to unpredictable failure patterns. While there are a number of well‐established failure analysis (physics‐of‐failure) models for individual components, these models do not hold good for complex systems as their failure behaviors may be totally different. Failure analysis of individual components does consider the environmental interactions but is unable to capture the system interaction effects on failure behavior. These models are based on the assumption of independent failure mechanisms. Dependency relationships and interactions of components in a complex system might give rise to some new types of failures that are not considered during the individual failure analysis of that component. This paper presents a general framework for failure modes and effects analysis (FMEA) to capture and analyze component interaction failures. The advantage of the proposed methodology is that it identifies and analyzes the system failure modes due to the interaction between the components. An example is presented to demonstrate the application of the proposed framework for a specific product architecture (PA) that captures interaction failures between different modules. However, the proposed framework is generic and can also be used in other types of PA. Copyright © 2007 John Wiley & Sons, Ltd.     

Photovoltaics in the assessment of wireless sensor network reliability with changing environmental conditions

This paper presents a reliability assessment of a wireless sensor network (WSN) equipped with mini photovoltaic cells (PV‐WSN) under natural environmental conditions while accounting for different types of system failures. In particular, our assessment considers the hardware specifications of the sensors, photovoltaic (PV) specifications, the use of rechargeable batteries, communication protocols, and various elements required for efficient detection of environmental conditions. We accomplished this by developing a simulator that generated data for 2 broad WSN conditions: (1) WSN without PV and (2) WSN with PV. The dynamic source routing protocol was employed for these simulations, and the following variables were assessed for both conditions: WSN reliability, the impact of energy consumption on the network, and the types of failures that lead to sensor unavailability. The following assumptions were made to run the simulation: the distribution of WSN nodes is random, with 1 sink node per rectangular cluster, the sensor nodes are structurally and functionally identical, environmental interference and suboptimal orientation impair PV cell recharge capacity randomly, and no communication loss occurs. Our reliability assessment assumed extreme environmental conditions and further made assessments of component reliability that included the following parameters: sensor and PV cell hardware specifications, the rechargeable nature of PV cell batteries for different sensor activity states, the availability of sunlight for powering PV cells, and the energy efficiency of PV cells. We found that network lifetime was prolonged for the PV‐WSN condition over the WSN without PV condition, introducing a role for PV cells as potential energy sources for WSNs.     

桥梁系统地震易损性分析的混合Copula函数方法

为了在桥梁系统易损性分析中考虑构件地震需求之间相关性的影响,采用贝叶斯加权平均方法构造混合Copula函数,将构件地震需求之间的相关结构和各构件的边缘分布函数进行分离;结合增量动力分析,建立了桥墩、支座等单个构件的易损性曲线及联合分布函数,提出了考虑构件地震需求相关性的桥梁系统易损性分析方法。结果表明:混合Copula函数能够准确描述构件地震需求间上、下尾相关并存的非对称相关结构,简化构件地震需求联合分布函数的建模过程;与Monte Carlo抽样方法相比,二者得到的桥梁系统易损性吻合良好,且混合Copula函数方法避免了大量的数值抽样,显著提高系统易损性分析的计算效率。     

大气相位屏的协方差冲激函数产生法

提出正交基为冲激函数的协方差法,在Matlab中仿真实现符合大气湍流统计分布(vonKarman谱和Kolmogorov谱)的大气相位屏。协方差冲激函数产生法对连续波前相位进行冲激函数正交分解,根据正交分解系数的协方差模拟平行光通过大气后的瞬时波前相位。计算多帧相位屏的波前结构函数,与理论值比对,验证产生大气相位屏的正确性。根据比对结果得,该方法产生的相位屏精度高,对于Kolmogorov谱,该方法产生的大气相位屏的波前结构函数误差约为理论波前结构函数均值的1%。     

Bayesian decision analysis and reliability certification

Reliability certification is set as a problem of Bayesian Decision Analysis. Uncertainties about the system reliability are quantified by assuming the parameters of the models describing the stochastic behavior of components as random variables. A utility function quantifies the relative value of each possible level of system reliability after having been accepted or the opportunity loss of the same level if the system has been rejected. A decision about accepting or rejecting the system can be made either on the basis of the existing prior assessment of uncertainties or after obtaining further information through testing of the components or the system at a cost. The concepts of value of perfect information, expected value of sample information and the expected net gain of sampling are specialized to the reliability of a multicomponent system to determine the optimum component testing scheme prior to deciding on the system's certification. A component importance ranking is proposed on the basis of the expected value of perfect information about the reliability of each component. The proposed approach is demonstrated on a single component system failing according to a Poisson random process and with natural conjugate prior probability density functions (pdf) for the failure rate and for a multicomponent system under general assumptions.