Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden ...for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO–HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence.
•Two optimization algorithms are proposed as novel hybrid FORM in structural reliability analysis.•Local adjusting process is proposed in hybrid FORM methods of EPSO and PSO–HS.•PSO–HS and EPSO compared with PSO, HS, IHS, IPSO, LS-PSO and six FORM algorithms.•Proposed methods are more efficient than FORM for high-dimensional problems.
Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy ...probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.
•Demand-based warm standby systems with capacity storage are modeled.•Different utilization sequences of warm standby and stored capacity are considered.•Multi-valued decision diagram is proposed for ...system reliability evaluation.•Chronological characteristics of warm standby activation are embedded.•The method allows systems with arbitrary time-to-failure distributions.
Warm standby is an energy-saving redundancy technique that consumes less energy than a conventional hot standby method. It can be naturally integrated with an energy storage technique to enhance system reliability. However, the integration approach of both techniques and the advantage it affords to system reliability have not been reported in literature. This study resolves this limitation by formulating a novel reliability model for demand-based warm standby systems with capacity storage. In this model, the chronological characteristics of warm standby components are explicitly explored before and after their activation. Moreover, different utilization sequences of warm standby components and storage components are embedded to analyze the effects of these sequences on system reliability. A multi-valued decision diagram is developed to evaluate system reliability considering successful activation probabilities of warm standby components. This diagram is applicable to arbitrary component lifetime distributions and warm standby systems. Numerical examples are presented to verify the application of the proposed methodology.
•A reliability method combining adaptive global metamodel and probability density evolution method is proposed.•Adaptive sampling technique for global metamodeling is more superior to one-stage ...sampling technique.•The AGM-PDEM has a better applicability than the AGM-MCS.•Accuracy of reliability method can be guaranteed by specifying moderate stopping condition in adaptive sampling process.
An efficient reliability method, i.e., AGM-PDEM, is presented which combines the adaptive global metamodel (AGM) and the probability density evolution method (PDEM). The global metamodel is firstly constructed using an adaptive sampling technique and employed to represent the relationship between the equivalent extreme value (EEV) of responses and basic random variables of stochastic systems. The EEVs of representative point sets with enlarged size are approximated by the constructed surrogate model. Reliability of the stochastic systems then can be readily obtained by virtue of the PDEM. To demonstrate the accuracy and efficiency of the proposed method in metamodeling response surface and in assessing system reliability, the global metamodeling of three analytical functions with different nonlinear features, and the reliability analysis of an eight-story shear frame with different random parameters related to the performance level are addressed. A comparative study against the direct Monte Carlo simulation (MCS) with the established metamodel is carried out as well. Numerical results demonstrate that the adaptive sampling technique for global metamodeling is more superior by comparison with the one-stage sampling technique. The AGM-PDEM has a better applicability than the AGM-MCS in dealing with the problems of both moderate and small failure probabilities encountered in engineering structures. Furthermore, both the fidelity of global metamodel and the accuracy of the proposed reliability method can be guaranteed by specifying a moderate stopping condition in the adaptive sampling process. Therefore, the proposed AGM-PDEM exhibits a satisfactory accuracy and a high efficiency for the reliability assessment of structural systems in practice.
Due to its advantages of simplicity and high efficiency, Hasofer–Lind and Rackwitz–Fiessler (HL–RF) method is widely used in structural reliability analysis. However, it may encounter difficulties in ...convergence, such as periodic oscillation, bifurcation and chaos when complex performance functions are involved. In this study, a new efficient and robust method is proposed to deal with the convergence problem and is applied to reliability-based design optimization (RBDO). The strategy is to modify the search direction of HL–RF to make it adaptable to changes, thereby reducing the risk of periodic oscillations. A step size adjustment formula is then established to adaptively adjust the finite-step size to prevent bifurcation or chaos. In addition, the proposed method is integrated into double loop method (DLM) to strengthen the robustness and efficiency of DLM for complex RBDO problems. The efficiency and robustness of the proposed method are demonstrated by comparison with other existing first order reliability methods (FORMs) through five numerical examples and two structural examples. Two complex RBDO problems also show that DLM based on the proposed method has the ability to solve complex RBDO problems.
•A new efficient and robust structural reliability method is proposed.•The search direction of HL–RF method is modified.•A new adaptive step size adjustment formula is established.•The proposed method improves the efficiency and robustness of FORM method.•The proposed method is applied to solve complex RBDO problems.
•Proposed a fast reliability evaluation method for integrated power-gas system (IPGS).•Presented a novel consequence analysis model of IPGS failures based on graph theory.•Proposed a tailor-made ...importance sampling algorithm for IPGS reliability evaluation.•Reliability indices are developed in the aspects of system, customers and components.
The supply reliability is a vital concern in the planning of integrated power-gas systems (IPGS). Previous reliability evaluation approaches of IPGS bring massive computational burdens due to the complex consequence (status) analysis model and numerous status samples in Monte Carlo simulation (MCS). In this paper, a systematic assessment approach is proposed to evaluate the supply reliability of IPGS rapidly. Firstly, a novel optimal load shedding model of IPGS is presented based on the stochastic capacity network model of gas system and the power flow model, which reduces the computational complexity of consequence analysis. Then, a tailor-made importance sampling (IS) method based on cross-entropy is proposed for IPGS to improve the efficiency of MCS. Through evaluating the criticality of training samples, the IS method accordingly alters the unavailability parameters of electricity and gas components, so that crucial risk events of IPGS are sampled more frequently in MCS. Furthermore, reliability indices of IPGS are developed in three hierarchies: system reliability, customer availability and component importance, which provide comprehensive references for system planners. Finally, numerical simulations are performed on two IPGS cases and the results validate the proposed approach significantly improves the computational efficiency of supply reliability evaluation for IPGS.
Belief Reliability for Uncertain Random Systems Zhang, Qingyuan; Kang, Rui; Wen, Meilin
IEEE transactions on fuzzy systems,
2018-Dec., 2018-12-00, 20181201, Letnik:
26, Številka:
6
Journal Article
Recenzirano
Measuring system reliability by a reasonable metric is a common problem in reliability engineering. Since real systems are usually uncertain random systems affected by both aleatory and epistemic ...uncertainties, existing reliability metrics are unreliable. This paper proposes a general reliability metric, called belief reliability metric, to cope with the problem. In this paper, the belief reliability is defined as the chance that a system state is within a feasible domain. Mathematically, the metric can degenerate to either probability theory-based reliability, which copes with aleatory uncertainty, or uncertainty theory-based reliability, which considers the effect of epistemic uncertainty. Based on the proposed metric, some commonly used belief reliability indexes, such as belief reliability distribution, mean time to failure, and belief reliable life, are introduced. We also develop system belief reliability formulas for different systems configurations. To further illustrate the formulas, a real case study is performed.
•Reliability analysis model for dynamic systems with CCF based on DTBN.•DTBN models are developed for a parallel system with cold and warm spare parts.•Logical relationship between failure states is ...examined to obtain CPT of DTBN node.•Impact of CCFs on a digital safety-level DCS of NPPs is studied by proposed method.
The dynamic and dependant behaviors are typical characteristics of modern complex systems, whose reliability is often improved through the design of multichannel parallel structures. The existence of common cause failures (CCFs) has a significant impact on system reliability. A reliability analysis model is proposed for dynamic systems with CCFs based on discrete-time Bayesian networks (DTBNs). The system operating time is dispersed into several time intervals, and the component failures are divided into independent and CCF states. Dynamic systems with cold and warm spare parts are examined to determine the modelling methodology and conditional probability tables (CPTs) of Bayesian network (BN) nodes. The reliability calculation is realised through the Bayesian inference mechanism. The model is applied to the CCF analysis and fault diagnosis of a digital safety-level distributed control system (DCS) of nuclear power plants (NPPs) to prove the effectiveness and feasibility of the method.
To maximize the reliability of a system, the traditional reliability–redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper ...proposes an advanced RRAP that also considers the optimal redundancy strategy, either active or cold standby. In addition, new examples are presented for it. Furthermore, the exact reliability function for a cold standby redundant subsystem with an imperfect detector/switch is suggested, and is expected to replace the previous approximating model that has been used in most related studies. A parallel genetic algorithm for solving the RRAP as a mixed-integer nonlinear programming model is presented, and its performance is compared with those of previous studies by using numerical examples on three benchmark problems.
•Optimal strategy is proposed to solve reliability redundancy allocation problem.•The redundancy strategy uses parallel genetic algorithm.•Improved reliability function for a cold standby subsystem is suggested.•Proposed redundancy strategy enhances the system reliability.
With the increasing penetration of wind power, reliable and cost-effective wind energy production is of more and more importance. As one of the common configurations, the doubly fed induction ...generator based partial-scale wind power converter is still dominating in the existing wind farms, and its reliability assessment is studied considering the annual wind profile. According to an electro-thermal stress evaluation, the time-to-failure of the key power semiconductors is predicted by using lifetime models and Monte Carlo based variation analysis. Aiming for the system-level reliability analysis, a reliability block diagram can be used based on Weibull distributed component-level reliability. A case study of a 2 MW wind power converter shows that the optimal selection of power module may be different seen from the reliability perspective compared to the electrical stress margin. It can also be seen that the B 1 lifetimes of the grid-side converter and the rotor-side converter deviate a lot by considering the electrical stresses, while they become more balanced by using an optimized design strategies. Thus, the system-level lifetime increases significantly with an appropriate design of the back-to-back power converters.
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