System Reliability Analysis Method Based on T-S FTA and HE-BN

For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.

System Reliability Analysis for Truss Structures Based on Automatic Updated Model

Multiple failure modes tend to be identified in the reliability analysis of a redundant truss structure.This identification process involves updating the model for identifying the next potential failure members.Herein we intend to update the finite element model automatically in the identification process of failure modes and further perform the system reliability analysis efficiently.This study presents a framework that is implemented through the joint simulation of MATLAB and APDL and consists of three parts:reliability index of a single member,identification of dominant failure modes,and system-level reliability analysis for system reliability analysis of truss structures.Firstly,RSM(response surface method)combines with a constrained optimization model to calculate the reliability indices ofmembers.Then theβ-unzipping method is adopted to identify the dominant failuremodes,and the system function in MATLAB,as well as the EKILL command in APDL,is used to facilitate the automatic update of the finite element model and realize load-redistribution.Besides,the differential equivalence recursion algorithmis performed to approximate the reliability indices of failuremodes efficiently and accurately.Eventually,the PNET(probabilistic network evaluation technique)is used to calculate the joint failure probability as well as the system reliability index.Two illustrative examples demonstrate the accuracy and efficiency of the proposed system reliability analysis framework through comparison with corresponding references.

System Reliability Analysis of an Offshore Jacket Platform

This study investigates strategies for solving the system reliability of large three-dimensional jacket structures.These structural systems normally fail as a result of a series of different components failures.The failure characteristics are investigated under various environmental conditions and direction combinations.Theβ-unzipping technique is adopted to determine critical failure components,and the entire system is simplified as a series-parallel system to approximately evaluate the structural system reliability.However,this approach needs excessive computational effort for searching failure components and failure paths.Based on a trained artificial neural network(ANN),which can be used to approximate the implicit limit-state function of a complicated structure,a new alternative procedure is proposed to improve the efficiency of the system reliability analysis method.The failure probability is calculated through Monte Carlo simulation(MCS)with Latin hypercube sampling(LHS).The features and applicability of the above procedure are discussed and compared using an example jacket platform located in Chengdao Oilfield,Bohai Sea,China.This study provides a reference for the evaluation of the system reliability of jacket structures.

Importance measure of system reliability upgrade for multi-state consecutive k-out-of-n systems

Importance measures in reliability systems are used to identify weak components in contributing to a proper function of the system. Traditional importance measures mainly concerned the changing value of the system reliability caused by the change of the reliability of the component, and seldom considered the joint effect of the probability distribution, improvement rate of the object component. This paper studies the rate of the system reliability upgrading with an improvement of the component reliability for the multi-state consecutive k-out-of-n system. To verify the multi-state consecutive k-out-of-n system reliability upgrading by improving one component based on its improvement rate, an increasing potential importance （IPI） and its physical meaning are described at first. Secondly, the relationship between the IPI and Birnbaum importance measures are discussed. And the IPI for some different improvement actions of the component is further discussed. Thirdly, the characteristics of the IPI are analyzed. Finally, an application to an oil pipeline system is given.

Bayesian method for system reliability assessment of overlapping pass/fail data

For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps： fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.

System Reliability Analysis of Offshore Strctures and Component Safety Rank Assessment

Offshore structures will encounter serious environmental load, so it is important to study the structural system reliability and to evaluate the structural component safety rank. In this paper, the bracnch-and-bound method is adopted to search the main failure path, and the Ditlevsen bound method is used to calculate the system failure probability. The structure is then assessed by the fuzzy comprehensive assessment method, which evaluates the structural component safety rank. The ultimate equation of the tubular cross- section is analyzed on the basis of ultimate stregnth analysis. The influence of effect coefficients on the structural system failure probability is investigated, and basic results are obtained. A general program for spatial frame structures by means of the above method is developed, and verified by the numerical examples.

DISTRIBUTED MONITORING SYSTEM RELIABILITY ESTIMATION WITH CONSIDERATION OF STATISTICAL UNCERTAINTY

Taking into account the whole system structure and the component reliability estimation uncertainty, a system reliability estimation method based on probability and statistical theory for distributed monitoring systems is presented. The variance and confidence intervals of the system reliability estimation are obtained by expressing system reliability as a linear sum of products of higher order moments of component reliability estimates when the number of component or system survivals obeys binomial distribution. The eigenfunction of binomial distribution is used to determine the moments of component reliability estimates, and a symbolic matrix which can facilitate the search of explicit system reliability estimates is proposed. Furthermore, a case of application is used to illustrate the procedure, and with the help of this example, various issues such as the applicability of this estimation model, and measures to improve system reliability of monitoring systems are discussed.

A Method for Analyzing System Reliability of Existing Jacket Platforms

Owing to the ageing of the existing structures worldwide and the lack of codes for the continued safely management of structures during their lifetime, it is very necessary to develop a tool to evaluate their system reliability over a time interval. In this paper, a method is proposed to analyze system reliability of existing jacket platforms. The influences of dint, crack and corrosion are considered. The mechanics characteristics of the existing jacket platforms to extreme loads are analyzed by use of the nonlinear mechanical analysis. The nonlinear interaction of pile-soil-structure is taken into consideration in the analysis. By use of FEM method and Monte Carlo simulation, the system reliability of the existing jacket platforul can be obtained. The method has been illustrated through application to BZ28-1 three jacket platforms which have operated for sixteen years. Advantages of the proposed method for analyzing the system reliability of the existing jacket platform is also highlighted.

System reliability evaluation method considering physical dependency with FMT and BDD analytical algorithm

Recently, the physics-of-failure(PoF) method has been more and more popular in engineering to understand the failure mechanisms(FMs) of products.However, due to the lack of system modeling methods and problem-solving algorithms,the information of FMs cannot be used to evaluate system reliability.This paper presents a system reliability evaluation method with failure mechanism tree(FMT) considering physical dependency(PDEP) such as competition, trigger, acceleration, inhibition, damage accumulation, and parameter combination.And the binary decision diagram(BDD) analytical algorithm is developed to establish a system reliability model.The operation rules of ite operators for generating BDD are discussed.The flow chart of system reliability evaluation method based on FMT and BDD is proposed.The proposed method is applied in the case of an electronic controller drive unit.Results show that the method is effective to evaluate system reliability from the perspective of FM.

Assessment of Multiple Working Condition System Reliability with Agent-Based Simulation Method

The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the characters of system reliability under the complex mission profile.In order to solve the problem,an agentbased simulation method was used to assess reliability for complex systems with various random working conditions.A multi-working condition simulation agent(MA)was designed and used to simulate the random transferring process of working conditions of system,and it cooperated with system simulation agents(SAs)and unit simulation agents(UAs)to realize system mission reliability(MR)simulation.Through simulation experiments,effect of multiple working conditions mission on the reliability of system was analyzed by comparing with the basic reliability condition.Feasibility and efficiency of the method were proved through simulation experiments of the case system.The research result provides a viable and useful method and a solution for MR analysis and assessment of complex systems in multi-working conditions,which can help to evaluate the reliability of operating system orienting to the practical mission and environment,and it is meaningful for the reliability analysis and the design of complex systems.

System reliability evaluation of long railway subgrade slopes considering discrete instability

This paper develops a dual-indicator discrete method(DDM)for evaluating the system reliability performance of long soil subgrade slopes.First,they are segmented into many slope sections using the random finite element method,to ensure each section statistically contains one potential local instability.Then,the k-out-of-n system model is used to describe the relationship between the total number of sections n,the acceptable number of failure sections m,the reliability of sections R_(sec),and the system reliability R_(sys).Finally,m and R_(sys)are jointly used to assess the system reliability performance.For cases lacking spatial data of soil properties,a simplified DDM is provided in which long subgrade slopes are segmented by the empirical value of section length and R_(sec)is substituted by that of crosssections taken from them.The results show that(1)DDM can provide the probability that the actual number of local instabilities does not exceed a desired threshold.(2)R_(sys)decreases with increasing n or decreasing R_(sec);that is,it is likely to encounter more local instabilities for longer or weaker subgrade slopes.n is negatively related to the horizontal scale of fluctuation of soil properties and positively related to the total length of subgrade slopes L.(3)When L is sufficiently large,there is a considerable opportunity to meet local instabilities even if R_(sec)is large enough.

Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA)

A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.

System reliability-based robust design of deep foundation pit considering multiple failure modes

Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main challenges.To address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,China.The proposed method included two parts:system reliability model and robust design method.Back Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability analysis.The common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system reliability.Furthermore,based on the system reliability model,a robust design method is developed.This method aims to find cost-effective design points.To solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is adopted.The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm.The proposed method has a good performance in locating the balanced design point between safety and construction cost.Moreover,the proposed method can provide design points with reasonable stiffness distribution.

Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.

Robust N−k Security-constrained Optimal Power Flow Incorporating Preventive and Corrective Generation Dispatch to Improve Power System Reliability

As extreme weather events have become more frequent in recent years,improving the resilience and reliability of power systems has become an important area of concern.In this paper,a robust preventive-corrective security-constrained optimal power flow(RO-PCSCOPF)model is proposed to improve power system reliability under N−k outages.Both the short-term emergency limit(STL)and the long-term operating limit(LTL)of the post-contingency power flow on the branch are considered.Compared with the existing robust corrective SCOPF model that only considers STL or LTL,the proposed ROPCSCOPF model can achieve a more reliable generation dispatch solution.In addition,this paper also summarizes and compares the solution methods for solving the N−k SCOPF problem.The computational efficiency of the classical Benders decomposition(BD)method,robust optimization(RO)method,and line outage distribution factor(LODF)method are investigated on the IEEE 24-bus Reliability Test System and 118-bus system.Simulation results show that the BD method has the worst computation performance.The RO method and the LODF method have comparable performance.However,the LODF method can only be used for the preventive SCOPF and not for the corrective SCOPF.The RO method can be used for both.

A Bayesian approach for integrating multilevel priors and data for aerospace system reliability assessment

This paper investigates Bayesian methods for aerospace system reliability analysis using various sources of test data and expert knowledge at both subsystem and system levels. Four sce- narios based on available information for the priors and test data of a system and/or subsystems are studied using specific Bayesian inference techniques. This paper proposes the Bayesian melding method for integrating subsystem-level priors with system-level priors for both system- and subsystem-level reliability analysis. System and subsystem reliability outcomes are compared under different scenarios. Computational challenges for posterior inferences using the sophisticated Bayesian melding method are addressed using Markov Chain Monte Carlo （MCMC） and adaptive Sam- piing Importance Re-sampling （SIR） methods. A case study with simulation results illustrates the applications of the proposed methods and provides insights for aerospace system reliability analysis using available multilevel information.

Operation Strategies for Coordinating Battery Energy Storage with Wind Power Generation and Their Effects on System Reliability

The variability of wind power generation requires the allocation of a flexible energy reserve which is capable of compensating for possible imbalances between the load and generation. To reduce the variability of wind power generation and loss of load in generation deficit, we propose operation strategies for coordinating battery energy storage with wind power generation. The effects of the operation strategies on system reliability are evaluated by the developed computation model that represents the main aspects and operation limitations of the batteries. The performance evaluation of the power system is based on the composite reliability indices of loss of load probability(LOLP) and expected energy not supplied(EENS), which is calculated through sequential Monte Carlo simulation. Tests are performed by the developed model with a tutorial system consisting of five busbars and the IEEE RTS system. The results show that the use of large-scale batteries is an alternative to physically guarantee the wind power plants and to act as an operation reserve to reduce the risk of loss of load.

Reliability Management for Information System

An integrated intelligent management is presented to help organizations manage many heterogeneous resources in their information system. A general architecture of management for information system reliability is proposed, and the architecture from two aspects, process model and hierarchical model, described. Data mining techniques are used in data analysis. A data analysis system applicable to real-time data analysis is developed by improved data mining on the critical processes. The framework of the integrated management for information system reliability based on real-time data mining is illustrated, and the development of integrated and intelligent management of information system discussed.

Optimal Allocation of Fleet's Spare Parts for System Reliability

Allocation of fleet's spare parts is rarely studied due to its complexity. However, this task is extremely important because the warship's service level highly relies on the maintenance logistics' level. In this study, the readiness ratio is proposed as a critical index in measuring the system's reliability. A well-established mathematical model adopting the optimization method of spare part allocation is also introduced. The objective is to minimize the number of each spare part while satisfying the fleet's system reliability. The fault tree analysis(FTA) is applied to analyze the system's failure logic and stratify the units on ship. As a result, the strategy of spare part sharing can be introduced in detail. The solution algorithm is developed, and the simulation experiments to obtain the key parameters are conducted. The proposed model and algorithm are applied to an actual fleet of two warships, and results show that the method above is feasible and can be directly applied into practice.

SYSTEM RELIABILITY OF ASSURED ACCURACY RATE FOR MULTI-STATE COMPUTER NETWORKS FROM SERVICE LEVEL AGREEMENTS VIEWPOINT

From the viewpoint of service level agreements, the transmission accuracy rate is one of critical performance indicators to assess internet quality for system managers and customers. Under the assumption that each arc＇s capacity is deterministic, the quickest path problem is to find a path sending a specific of data such that the transmission time is minimized. However, in many real-life networks such as computer networks, each arc has stochastic capacity, lead time and accuracy rate. Such a network is named a multi-state computer network. Under both assured accuracy rate and time constraints, we extend the quickest path problem to compute the probability that d units of data can be sent through multiple minimal paths simultaneously. Such a probability named system reliability is a performance indicator to provide to managers for understanding the ability of system and improvement. An efficient algorithm is proposed to evaluate the system reliability in terms of the approach of minimal paths.