On Improved Confidence Bounds for System Reliability

A flexible procedure is described and demonstrated to determine approximate confidence intervals for system reliability when there is uncertainty regarding component reliability information. The approach is robust, and applies to many system-design configurations and component time-to-failure distributions, resulting in few restrictions for the use of these confidence intervals. The methods do not require any parametric assumptions for component reliability or time-to-failure, and allows type-I or -II censored data records. The confidence intervals are based on the variance of the component and system reliability estimates and a lognormal distribution assumption for the system reliability estimate. This approach applies to any system design which can be decomposed into series and/or parallel connections between the components. To evaluate the validity of the confidence limits, numerous simulations were performed for two hypothetical systems with different data sample-sizes and confidence levels. The test cases and empirical results demonstrate that this new method for estimating confidence intervals provides good coverage, can be readily applied, requires only minimal computational effort, and applies for a much greater range of design configurations and data types compared to other methods. For many design problems, these confidence intervals are preferable because there is no requirement for an exponential time-to-failure distribution nor are component data limited to binomial data.

The paper highlights the reliability estimation tool for power system's reliability estimation study. The reliability estimation tool was applied to the different configurations of Uninterruptible Power Supply (UPS) systems Capacity, Isolated Redundant, Parallel Redundant, Distributed Redundant, and System plus System Redundant configurations. UPS reliability parameters such as failure rates, Mean Time Between Failures, and Availability can be estimated from the proposed system's reliability estimation methods. The suggested reliability estimation methods in this paper are the Reliability Block Diagram (RBD) and the Monte-Carlo simulation methods. The UPS system reliability estimation study found that the System plus System Redundant configuration gives the highest reliability and availability values compared to all other configurations. Studies has been conducted to investigate the effects of the reliability level of every component in the UPS system onto the overall system's reliability, thus making it possible to rank their contribution to system's reliability and thereby useful in establishing the area of improvement in the design of the highest UPS system's reliability.

The University of Bristol is developing the PROVE (Pointable Radiometer for Observation of Volcanic Emissions) Pathfinder payload, a part of a 6U CubeSat hoped to launch in the next year. PROVE Pathfinder's aim is to image volcanic ash plumes from several different locations and aspects along its orbital trajectory. These images will be used to gain more knowledge of the ash cloud, useful in protecting civil aviation in the case of large-scale volcanic eruptions with ash associated. As CubeSats have historically suffered from a high failure rate, a thorough understanding of the reliability of the PROVE Pathfinder system is essential to mitigating the mission risk. This paper presents the analysis, results and recommended failure mitigation strategies from a reliability study carried out on the PROVE Pathfinder payload system. Several different analysis approaches were used to study the reliability of the system. These included Failure Mode Effects and Criticality Analysis (FMECA), Fault Tree Analysis (FTA), Monte Carlo simulation and a reliability model of PROVE Pathfinder devised using Model Based Systems Engineering (MBSE). Various estimates of system reliability were produced, with a final figure of 80% reliability. This estimate took account of the numerous mitigation strategies identified during the study, including cold redundancy in payload controllers and aluminum radiation shielding. While some discrepancies in qualitative reliability estimates were found, the insight gained during the study greatly assisted with identifying mitigation strategies and generally improved system reliability. A framework for reliability analysis approaches and mitigation strategy identification for general use on spacecraft systems is proposed, with opportunity for future development.

Monte Carlo methods are useful for a number of situations in reliability. First, for very complex systems, it is difficult to determine the system reliability even if the component reliabilities are known. This can be done by simulating the performance (operating/failed) of each component, and determining the status of the system given the performances of these components. Second, the system reliability can be estimated when component reliabilities are estimated from pass/fail data. Uncertainties in the resulting estimate of system reliability can be obtained via Monte Carlo simulation. Third, many questions of availability (or other measures of system performance) can be treated using queuing theory. Since queuing models can become intractable, especially when times between events or repair times are not exponential random variables, Monte Carlo methods can be used.

The network theory is widely applied to improve the reliability of a complex electromechanical system. In this application, system reliability assessment with network theory has been paid a great deal of attention. Because of instrument malfunctions, staff omissions, imperfect inspection strategies, and complex structures, field failure data are often subject to interval censoring, making the holistic reliability assessment becomes a difficult task. Most traditional methods assume reliability of critical components or partial reliability as system reliability, which may cause a large bias in system reliability estimation. This paper proposes a novel method to evaluate and predict the system reliability of a complex electromechanical system subject to the insufficient fault data problem from a network perspective. First, the system modeling based on network theory is developed to describe the topology of a holistic system. Second, interval‐valued intuitionistic hesitant fuzzy number is proposed in order to solve insufficient data for single component. Then, a new measure—comprehensive reliability—that can reflect the reliability of nodes in combination with functional properties and topological properties, which are formulated by failure data and network model, respectively, is constructed for system reliability assessment. Subsequently, an improved system reliability model based on percolation theory is given in terms of comprehensive reliability of nodes. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for traction system are implemented.

<div class="section abstract"><div class="htmlview paragraph">A study of the cockpit information system architecture of current single-engine single-pilot aircraft was performed to establish a baseline for the evaluation of the reliability of new cockpit systems being developed through the Advanced General Aviation Transport Experiments (AGATE) program. That study defines a “typical” General Aviation (GA) cockpit information system architecture consisting of 38 components making up 32 subsystems. It also developed a reliability (fault tree) model for the system and utilized a proprietary analysis tool to compute system reliability.</div><div class="htmlview paragraph">Fault tree reliability models have gained wide acceptance since their introduction in the 1960’s to analyze the probability of success of military defense systems. Fault trees use logic gates to express the relationships between failures of the components and resulting failures of subsystems and of the system.</div><div class="htmlview paragraph">In a subsequent study an analytical model corresponding to the specific cockpit system reliability model was developed and implemented in a spreadsheet format. In addition to the system reliability equation, all of the subsystem reliability equations were also derived. From the analytic reliability model, the sensitivities of all of the subsystem and system reliabilities to changes in the component reliabilities were derived. These equations were also implemented in the spreadsheet tool. The resulting tool allows a user to modify the component reliability parameters and compute and display the system and subsystem reliabilities and the sensitivities of these reliabilities to changes in the component reliabilities.</div><div class="htmlview paragraph">This paper describes the single-engine single-pilot aircraft cockpit information system reliability model, explains the derivation of the analytical model for reliability and sensitivity computation, discusses the insight obtained from the analytical model, and presents the application of the spreadsheet tool.</div></div>

This study develops a novel Monte Carlo simulation (MCS) approach to estimate system reliability for a stochastic production system with finite buffer storage. System reliability indicates the probability of all workstations providing sufficient capacities to satisfy a specified demand, as well as that all buffer stations are not running out of storage. First, buffer stations are modeled in a stochastic production network (SPN) model and their storage usage is analyzed based on the network-structured SPN. Second, an MCS is developed to generate the system state and to check the storage usage of buffer stations to determine whether the demand can be satisfied. After repeated simulations, the system reliability of the SPN can be estimated. Experimental results show that the proposed MCS approach is effective and efficient in estimating system reliability with reasonable quality for an SPN within a reasonable time. More importantly, system reliability will be overestimated with infinite buffer storage, and thus, it is worth studying finite buffer storage.

A new method for estimating the reliability of structural systems is proposed. The method is based on the use of Monte Carlo simulation. Monte Carlo based methods for system reliability analysis has several attractive features, the most important being that the system failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. The disadvantage of such methods is the amount of computational efforts that may be involved. However, by reformulating the reliability problem to depend on a parameter and exploiting the regularity of the failure probability as a function of this parameter, it is shown that a substantial reduction of the computational efforts involved can be obtained.

The probabilistic reliability estimation of complex system is complicated due to uncertainty of failure data, modeling or human failure. In this paper, reliability of components of complex network system is considered as fuzzy in nature to reduce the uncertainty. Trapezoidal fuzzy number is used to represent component's reliability of network system. Then the reliability of the network system is assembled with fuzzy reliability of components and evaluated by Zadeh's extension principle. Fuzzy reliability of the network system becomes an interval by α-cuts operation. Interval nonlinear programming is used to evaluate the optimum network's system reliability with interval valued cost constraint. The above approach is explained in details through a numerical example of very useful power system network of electrical engineering.

This research presents the creation of a new model for automating the generation of component and system reliability estimates from simulated field data for tightly coupled systems. The model utilizes the CMAC neural network architecture, which resembles the human cerebellum and is capable of approximating nonlinear functions. An analysis and testing of the network as a tool for reliability prediction of dependent components within an assembly has been performed. In order to evaluate the performance of the model, the network has been tested on simulated data and provided over 90% performance accuracy in learning non-linear functions that represent the dependency between components. This serves as a valuable tool for maintenance personnel faced with important and costly decisions regarding equipment maintenance policies.

Complex analysis and design of structures, especially landmark structures such as long-span bridges, have been conducted by many engineers and researchers. Currently, it is possible to collect more and precise monitoring data as well as to develop complex three-dimensional (3D) FEM models. These models, which can be calibrated using structural health-monitoring (SHM) data, can be used for the estimation of component and system reliability of bridges. However, the uncertainties related to the data, analysis, and nonstationary nature of the structural behavior need to be better incorporated by using a set of models that are continuously updated with monitoring data. This set of models constitutes a family as a result of the approach by which the models are obtained and the relationships among them. The objective of this paper is to explore the impact of uncertainty in predicting the system reliability obtained by a one-time, initially calibrated FEM model as well as by a family of FEM models continu...

Reliability estimation of mobile agent-based systems remains a difficult task due to the characteristics of mobile agents that include distributed and asynchronous execution, autonomy, and mobility. In this paper we present an analytical approach to estimate the reliability of mobile agent-based systems. We define the reliability of a mobile agent in terms of the processor execution time required to perform agent's tasks at each processing node and the transmission or communication time needed to transfer the agent from one processing node to another via a network to complete its mission. The failure of entire components causes failure of the agent. Simulation experiments have been conducted using stochastic Petri Nets modeling. We investigate the effect of different component reliability and availability models (processors and links) on the system throughput. The results we have obtained demonstrate the impact of different reliability and availability models on the system throughput.

This paper proposes a soft-error model for accurately estimating reliability of a computer system at the architectural level within reasonable computation time. The architectural-level soft-error model identifies which part of memory modules are utilized temporally and spatially and which single event upsets (SEUs) are critical to the program execution of the computer system at the cycle accurate instruction set simulation (ISS) level. The soft-error model is capable of estimating reliability of a computer system that has several memory hierarchies with it and finding which memory module is vulnerable in the computer system. Reliability estimation helps system designers apply reliable design techniques to vulnerable part of their design. The experimental results have shown that the usage of the soft-error model achieved more accurate reliability estimation than conventional approaches. The experimental results demonstrate that reliability of computer systems depends on not only soft error rates (SERs) of memories but also the behavior of software running in computer systems.

As the complexity of system structure increases, system reliability estimation is challenging for lack of time and cost. How to enhance the system reliability distribution for complex multi-level structures is of great value. Besides, when there exists reliability distribution information of subsystems or components, how to fully use the limited information of different system levels for the system reliability distribution estimation has great significance. Therefore, considering that Bayesian Networks can construct complex system structures intuitively as well as evaluate the system reliability effectively, and the Bayesian Melding Method can integrate the multi-level information adequately, a complex multi-level system reliability distribution estimation method is proposed. The complex system structure is first decomposed to many blocks by BN. The single block system reliability distribution is obtained based on the Bayesian Melding Method with information integrated and Bayesian Networks with reliability estimated. The final system reliability distribution is estimated by merging the distributions obtained in different blocks so as to enhance the system reliability distribution with more information from different levels. To demonstrate and verify the effectiveness of the proposed method, one numerical example is employed.

Ensuring reliable electric power to critical equipment is vital to the operation of many industrial, commercial, and military facilities. Accurate assessments of electrical system reliability are needed to determine if improvements are necessary and, in many cases, these assessments are required as a part of any new construction. However, it is generally impossible to perform actual testing on the system and hand calculations are also impossible for all but the most trivial systems. There are computer modeling tools which calculate system reliability or availability for electrical or mechanical systems based on the reliability of individual components, how those components are interconnected, and performance criteria for the system based on facility requirements. Other computer modeling tools are frequently cumbersome to use, require access to a mainframe computer, and base the results on suspect data complied in the 1970s. The Reliability and Availability Modeling Program (RAMP) is designed to perform reliability analysis using component operational and maintenance data on 242 items in the categories of power generation, power distribution, and HVAC. The authors describe the main features of RAMP, its software, and its use.