Predicting availability functions in time-dependent complex systems with SAEDES simulation algorithms

In this paper, we propose the use of discrete-event simulation (DES) as an efficient methodology to obtain estimates of both survival and availability functions in time-dependent real systems—such as telecommunication networks or distributed computer systems. We discuss the use of DES in reliability and availability studies, not only as an alternative to the use of analytical and probabilistic methods, but also as a complementary way to: (i) achieve a better understanding of the system internal behavior and (ii) find out the relevance of each component under reliability/availability considerations. Specifically, this paper describes a general methodology and two DES algorithms, called SAEDES, which can be used to analyze a wide range of time-dependent complex systems, including those presenting multiple states, dependencies among failure/repair times or non-perfect maintenance policies. These algorithms can provide valuable information, specially during the design stages, where different scenarios can be compared in order to select a system design offering adequate reliability and availability levels. Two case studies are discussed, using a C/C++ implementation of the SAEDES algorithms, to show some potential applications of our approach.     

多物流生产线系统的功能有效度分析

针对多物流生产线同时生产系统可靠性分析的难点及物流量预测不准确的问题，提出了准并联及功能有效度的概念，并分析得到了功能有效度的计算公式。为考虑物流的准并联系统的可靠性分析提供了科学的分析方法。     

New method to minimize the preventive maintenance cost of series–parallel systems

General preventive maintenance model for input components of a system, which improves the reliability to ‘as good as new,’ was used to optimize the maintenance cost. The cost function of a maintenance policy was minimized under given availability constraint. An algorithm for first inspection vector of times was described and used on selected system example. A special ratio-criterion, based on the time dependent Birnbaum importance factor, was used to generate the ordered sequence of first inspection times. Basic system availability calculations of the paper were done by using simulation approach with parallel simulation algorithm for availability analysis. These calculations, based on direct Monte Carlo technique, were applied within the programming tool Matlab. A genetic algorithm optimization technique was used and briefly described to create the Matlab's algorithm to solve the problem of finding the best maintenance policy with a given restriction. Adjacent problem, which we called ‘reliability assurance,’ was also theoretically solved, concerning the increase of the cost when asymptotic availability value conforms to a given availability constraint.     

Formal Specification and Quantitative Analysis of a Constellation of Navigation Satellites

Navigation satellites are a core component of navigation satellite‐based systems such as Global Positioning System, Global Navigation Satellite System and Galileo, which provide location and timing information for a variety of uses. Such satellites are designed for operating on orbit to perform tasks and have lifetimes of 10 years or more. Reliability, availability and maintainability analysis of systems has been indispensable in the design phase of satellites in order to achieve minimum failures or to increase mean time between failures and thus to plan maintenance strategies, optimise reliability and maximise availability. In this paper, we present formal models of both a single satellite and a navigation satellite constellation and logical specification of their reliability, availability and maintainability properties, respectively. The probabilistic model checker PRISM has been used to perform automated analysis of these quantitative properties. Copyright © 2014 John Wiley & Sons, Ltd.     

Monte Carlo-based assessment of system availability. A case study for cogeneration plants

The complexity of the modern engineering systems besides the need for realistic considerations when modeling their availability and reliability render analytic methods very difficult to be used. Simulation methods, such as the Monte Carlo technique, which allow modeling the behavior of complex systems under realistic time-dependent operational conditions, are suitable tools to approach this problem.The scope of this paper is, in the first place, to show the opportunity for using Monte Carlo simulation as an approach to carry out complex systems' availability/reliability assessment. In the second place, the paper proposes a general approach to complex systems availability/reliability assessment, which integrates the use of continuous time Monte Carlo simulation. Finally, this approach is exemplified and somehow validated by presenting the resolution of a case study consisting of an availability assessment for two alternative configurations of a cogeneration plant.In the case study, a certain random and discrete event will be generated in a computer model in order to create a realistic lifetime scenario of the plant, and results of the simulation of the plant's life cycle will be produced. After that, there is an estimation of the main performance measures by treating results as a series of real experiments and by using statistical inference to reach reasonable confidence intervals. The benefits of the different plant configurations are compared and discussed using the model, according to their fulfillment of the initial availability requirements for the plant.     

Analysis and optimization of weighted voting systems consisting of voting units with limited availability

The voting system studied consists of n voting units each either providing a binary decision (0 or 1) or abstaining from voting. The system output is 1 if the cumulative weight of all 1-opting units is at least a pre-specified fraction τ of the cumulative weight of all non-abstaining units. Otherwise, the system output is 0.In this paper, we study the effect of limited availability of the voting units on the entire voting system reliability. Two different types of systems are considered. In the system of type 1, the absence of unit output (unit unavailability) is interpreted by the system as abstention from voting. In the system of type 2, the unavailable state of the voting unit and its abstention from voting can be distinguished and the system parameters can be adjusted to optimize its performance for each combination of available units.There are two ways to improve reliability of weighted voting system consisting of units with the given output probability distribution: optimization of system parameters (weights of units and threshold factor value) and units availability enhancement (for example, by choosing proper maintenance policy). This paper shows a method of incorporating information about units' availability into a procedure for determining the optimal system parameters. It also presents a method for determining indices that measure importance of voting units availability for both types of systems. These indices indicate voting units for which efforts of availability enhancement are the most beneficial from the entire system reliability improvement point of view.The approach is based on using a universal generating function technique and optimization procedure presented in [5]. Examples are presented.     

Service‐level assurance in high‐availability multi‐unit systems using the M‐for‐N backup scheme

In this paper, we investigate service‐level assurance in high‐availability multi‐unit systems using the M‐for‐N backup scheme. M‐for‐N shared protection (backup) systems with priority control (i.e. prioritized protection switching and prioritized re‐housing of repaired units) can be applied to actual telecommunication devices that are subject to service‐level agreement (SLA) involving reliability measures. A priority level is assigned to each end user in such a system and the switching and unit re‐housing process is subject to the priority. The main contribution of this paper is to give a practical computation method of the user‐perceived availability under the priority control. Our case studies for real telecommunication systems reveal the effect of priority control on the user‐perceived availability. Copyright © 2011 John Wiley & Sons, Ltd.     

The robustness of markov reliability models

Markov models are an established part of current systems reliability and availability analysis. They are extensively used in various applications, including, in particular, electrical power supply systems. One of their advantages is that they considerably simplify availability evaluation so that the availability of very large and complex systems can be computed. It is generally assumed, with some justification, that the results obtained from such Markov reliability models are relatively robust. It has, however, been known for some time, that practical time to failure distributions are frequently non-exponential, particular attention being given in much reliability work to the Weibull family. Morover, recently additional doubt has been case on the validity of the Markov approach, both because of the work of Professor Kline and others on the non-exponentiality of practical repair time distribution, and because of the advantages to be obtained in terms of modelling visibility of the alternative simulation approach. In this paper we employ results on the ability of the k-out-of-n systems to span the coherent set to investigate the robustness of Markov reliability models based upon a simulation investigation of coherent systems of up to 10 identical components. We treat the case where adequate repair facilities are available for all components. The effects upon the conventional transient and steady-state measures of Weibull departures from exponentiality are considered. In general, the Markov models are found to be relatively robust, with alterations to failure distributions being more important than those to repair distributions, and decreasing hazard rates more critical than increasing hazard rates. Of the measures studied, the mean time to failure is most sensitive to variations in distributional shape.     

Aggregated Markov-based reliability analysis of multi-state systems under combined dynamic environments

Reliability of a system may differ greatly when operating under different environments. However, the existing works have either neglected the environment factor in system reliability analysis or considered this factor for binary systems or systems subject to a single environment (parameter). In this paper, we make contributions by modeling a multi-state system operating under hybrid dynamic environments affected by multiple environmental parameters. Different Markov chains with finite states are used to represent the random system behavior and dynamic environments, leading to an aggregated Markov process that models the overall system behavior. An effective approach based on state partitions and aggregations is suggested for assessing the system reliability indexes, including reliability, availability, multi-point availability, and environment-based reliability. A high-pressure homogenizer system is analyzed to demonstrate the proposed model and show the comparison of the reliability of system under fixed and dynamic environment.     

Integrating reliability optimization into chemical process synthesis

The growing need to achieve high availability for large integrated chemical process systems demands higher levels of system reliability at the operational stage. In these circumstances, it has become critical to consider the reliability aspects of a system and its components at the design stage. Traditional reliability/availability analysis methods and maintenance optimization frameworks, commonly applied at the design stage, are limited in their application, as in most of these methods the designer is required to specify the process system components, their connectivity and their reliabilities a priori. As a result, these traditional methods do not provide the flexibility to reconfigure a process or select initial reliabilities of equipment in a way that maximizes the inherent plant availability at the design stage. In this paper, we developed an optimization framework by combining the reliability optimization and process synthesis challenges and the combined optimization problem is posed as a mixed integer non-linear programming optimization problem. The proposed optimization framework features an expected profit objective function, which takes into account the trade-off between initial capital investment and the annual operational costs by supporting appropriate estimation of revenues, investment cost, raw material and utilities cost, and maintenance cost as a function of the system and its component availability. The effectiveness and usefulness of the proposed optimization framework is demonstrated for the synthesis of the hydrodealkylation process (HDA) process.     

Availability and reliability of k-out-of-(M+N):G warm standby systems

Redundancy or standby is a technique that has been widely applied to improving system reliability and availability in system design. In most cases, components in standby system are assumed to be statistically identical and independent. However, in many practical applications, not all components in standby can be treated as identical because they have different failure and repair rates. In this paper, one kind of such systems with two categories of components is studied, which is named k-out-of-(M+N):G warm standby system. In the system, one category of the components is of type 1 and the other type 2. There are M type 1 components and N type 2 components. Components of type 1 have a lower failure rate and are preferably repaired if there is one failed. There are r repair facilities available. By using Markov model, the system state transition process can be clearly illustrated, and furthermore, the solutions of system availability and reliability are obtained based on this. An example representing a power-generator and transmission system is given to illustrate the solutions of the system availability and reliability.     

A survey of software dependability

This paper presents on overview of the issues in precisely defining, specifying and evaluating the dependability of software, particularly in the context of computer controlled process systems. Dependability is intended to be a generic term embodying various quality factors and is useful for both software and hardware. While the developments in quality assurance and reliability theories have proceeded mostly in independent directions for hardware and software systems, we present here the case for developing a unified framework of dependability—a facet of operational effectiveness of modern technological systems, and develop a hierarchical systems model helpful in clarifying this view. In the second half of the paper, we survey the models and methods available for measuring and improving software reliability. The nature of software “bugs”, the failure history of the software system in the various phases of its lifecycle, the reliability growth in the development phase, estimation of the number of errors remaining in the operational phase, and the complexity of the debugging process have all been considered to varying degrees of detail. We also discuss the notion of software fault-tolerance, methods of achieving the same, and the status of other measures of software dependability such as maintainability, availability and safety.     

Combined hardware and software aspects of reliability

This paper presents the similarities and differences between hardware, software and system reliability. Relative contributions to system failures are shown for software and hardware and failure and recovery propensities are also discussed. Reliability, availability and maintainability (RAM) concepts have been broadly developed for software reliability than hardware reliability. Extending these software concepts to hardware and system reliability helps in examining the reliability of complex systems. The paper concludes with assurance techniques for defending against faults. Most of the techniques discussed originate in software reliability but apply to all aspects of a system. Also, the effects of redundancy on overall system availability are shown.     

Evaluation of full and degraded mission reliability and mission dependability for intermittently operated, multi-functional systems

Availability is one of the metrics often used in the evaluation of system effectiveness. Its use as an effectiveness metric is often dictated by the nature of the system under consideration. While some systems operate continuously, many others operate on an intermittent basis where each operational period may often involve a different set of missions. This is the most likely scenario for complex multi-functional systems, where each specific system mission may require the availability of a different combination of system elements. Similarly, for these systems, not only is it important to know whether a mission can be initiated, it is just as important to know whether the system is capable of completing such a mission. Thus, for these systems, additional measures become relevant to provide a more holistic assessment of system effectiveness. This paper presents techniques for the evaluation of both full and degraded mission reliability and mission dependability for coherent, intermittently operated multi-functional systems. These metrics complement previously developed availability and degraded availability measures of multi-functional systems, in the comprehensive assessment of system effectiveness.     

Assessment of availability of a Fluid Catalytic Cracking Unit through simulation

This paper presents a systematic approach to estimate the availability of process plants. The study includes a live problem at a Fluid Catalytic Cracking Unit (FCCU) of a refinery requiring high levels of availability for cost-effective operation. The system is modelled as a fault tree which is often used in the analysis of chemical process industries. A numerical evaluation of the fault tree assesses the characertistic safety parameters such as reliability and availability of the system. However, for large and complex systems, such analysis will normally require enormous computational effort, involving the breakdown of the fault tree into minimal cut sets. An alternative approach is to simulate the system using the Monte Carlo method. This paper presents an availability analysis of the Reactor/Regenerator system of the Fluid Catalytic Cracking Unit using the Monte Carlo simulation. The results of the simulation are validated by a comparison with the actual system. The method promises to be a useful tool for assessing the availability of complex systems with alternative configurations.     

Automatic creation of Markov models for reliability assessment of safety instrumented systems

After the release of new international functional safety standards like IEC 61508, people care more for the safety and availability of safety instrumented systems. Markov analysis is a powerful and flexible technique to assess the reliability measurements of safety instrumented systems, but it is fallible and time-consuming to create Markov models manually. This paper presents a new technique to automatically create Markov models for reliability assessment of safety instrumented systems. Many safety related factors, such as failure modes, self-diagnostic, restorations, common cause and voting, are included in Markov models. A framework is generated first based on voting, failure modes and self-diagnostic. Then, repairs and common-cause failures are incorporated into the framework to build a complete Markov model. Eventual simplification of Markov models can be done by state merging. Examples given in this paper show how explosively the size of Markov model increases as the system becomes a little more complicated as well as the advancement of automatic creation of Markov models.     

Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant

Power generators are critical assets in wastewater treatment plants (WWTPs) in Australia and many countries. Better managing the lifetime, minimising failures, improving reliability and availability, and reducing operating and maintenance costs of the power generation assets are still challenging topics for water utilities. This case study aims to develop power generation system reliability and availability modelling considering redundancy to minimise operation and maintenance costs. The two-parameter Weibull model was used to assess system reliability and availability to power generation engines in WWTPs. The Kaplan-Meier method (a time-driven estimation technique) and the log beta-Weibull model (which is suitable for modelling censored and uncensored data) were used to analyse and validate the modelling results. Shape and scale parameters of the Weibull models were estimated by maximising the log-likelihood function using non-linear optimisation. Hazard and reliability functions were calculated using the Weibull model. Results using two-parameter Weibull, Kaplan-Meier, and log beta-Weibull models display low reliability and high hazard rate over time, which was associated with spark plug failure due to a suboptimal start and stop operation strategy.     

Discussion on maintenance strategy, policy and corresponding maintenance systems in manufacturing

This paper presents a case study on aintenance at robotic car assembly lines, and further gives a discussion on how to form an optimum maintenance strategy, policy and corresponding maintenance systems in order to achieve high reliability, availability, safety and productivity of manufacturing systems. It stresses that it is technically possible and economically necessary to maintain the manufacturing systems in a preventive and predictive way by development of maintenance systems for early-warnings, fault-tracking and quick decisionmaking, with the trend towards automation, integration and openness.     

Explicit solutions for the response probability density function of nonlinear transformations of static random inputs

This work is the second paper of two companion ones. Both of them show the use of a new version of the Probabilistic Transformation Method (PTM) for finding the probability density function (pdf) of a limited number of response quantities in the transformations of static random inputs. This is made without performing multi-dimensional integrals of the response total joint pdf for saturating the non-interested variables. While in the first paper the linear transformations have been considered, in the present one some nonlinear systems are taken into account. In particular, first the case when the loads on a linear structural system are a nonlinear combination of static random inputs is studied. Then the attention is placed on the case of nonlinear structural systems, for which the new version of the PTM allows to determine approximated, but accurate, results.     

Approximate Reliability and Availability Models for High Availability and Fault‐tolerant Systems with Repair

Systems designed for high availability and fault tolerance are often configured as a series combination of redundant subsystems. When a unit of a subsystem fails, the system remains operational while the failed unit is repaired; however, if too many units in a subsystem fail concurrently, the system fails. Under conditions usually met in practical situations, we show that the reliability and availability of such systems can be accurately modeled by representing each redundant subsystem with a constant, ‘effective’ failure rate equal to the inverse of the subsystem mean‐time‐to‐failure (MTTF). The approximation model is surprisingly accurate, with an error on the order of the square of the ratio mean‐time‐to‐repair to mean‐time‐to‐failure (MTTR/MTTF), and it has wide applicability for commercial, high‐availability and fault‐tolerant computer systems. The effective subsystem failure rates can be used to: (1) evaluate the system and subsystem reliability and availability; (2) estimate the system MTTF; and (3) provide a basis for the iterative analysis of large complex systems. Some observations from renewal theory suggest that the approximate models can be used even when the unit failure rates are not constant and when the redundant units are not homogeneous. Copyright © 2004 John Wiley & Sons, Ltd.