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.
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.
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.
•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.
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.
•The contents of the PSFs of SPAR-H method have been improved, and the dependencies among the improved PSFs are investigated in this study.•The dependencies among the PSFs are modeled from the ...dynamic and nonlinear perspective.•The modeling of the dependencies of the PSFs improves the confidence of the estimation of the HEPs.
Performance shaping factors (PSFs) describe the influence of given contexts on human performance and are used to quantify human error probabilities (HEPs). HEPs are calculated within human reliability analysis, which is a part of probabilistic risk assessment. One challenge lies in the fact that the dependencies among PSFs are neglected, which leads to the poor estimation of HEPs. The objective of this research is to apply a system dynamics approach to the modeling and analysis of the dependencies of PSFs within the standardized plant analysis of risk–human reliability analysis method. Dependencies are built on the causalities of PSFs and are determined by the mutual information theory and the analytic hierarchy process.
The method is demonstrated on three human failure events. The first one is the failure of initiating reactor coolant system depressurization. The second one is the failure of throttling the high-pressure injection to reduce reactor coolant system pressure. The third one is the failure of diagnosing the steam generator tube rupture. The sensitive analysis results show that our model is reasonable and robust when dependencies are taken into account.
This paper proposes a comprehensive evaluation of stacked revenue generated from grid-connected energy storage systems (ESSs). The stacked revenue from an ESS cannot be calculated by merely ...aggregating the benefits from various applications (e.g., energy arbitrage, frequency regulation, and outage mitigation) as the ESS may not be available for all types of applications during the same time intervals. This is because a quantity committed to one market may not be committed to another. In this paper, different types of applications for grid-connected ESSs are identified, and a model incorporating component reliability, power system operation constraints, and storage system operation constraints is developed to evaluate the composite revenue generated from these applications. In this model, the types of applications of ESSs are prioritized according to their intended contributions and system operating conditions. Sequential Monte Carlo simulation is used for evaluating the reliability improvement and a quadratically constrained linear programing model is built for estimating the maximum revenue from arbitrage and regulation markets. The proposed method is demonstrated on the IEEE reliability test system using historical PJM price data.
Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been ...extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time-slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.
The identification of reliability-critical input vectors (RCIVs) is vital in the assessment and prediction of reliability boundaries for logic circuits. This article introduces an approach grounded ...in association rule analysis (ARA) to swiftly and efficiently identify RCIVs in both combinational and sequential circuits. The utilization of the ARA model for validating the circuit's associated primary inputs enhances accuracy while simultaneously reducing the complexity of RCIVs identification. Orienting the generation of new samples with associated inputs expedites the identification process. Quantifying circuit complexity enables the adaptive assignment of algorithmic parameters to circuits of diverse sizes. The construction of input sets facilitates a precise evaluation of the reliability of individual input vectors in sequential circuits. Experimental results on benchmark circuits illustrate that this approach achieves a mean accuracy of 0.9952, with Monte Carlo (MC) method serving as the reference, for small and medium-sized circuits, and require only 20.71% of MC's time overhead. The average coverage of 0.9884 surpasses the reference method by 1.8 times. The stability is 4.35 times higher with the random method on large scale circuits with 224624 gates and 6,642 primary inputs. Circuit designers can swiftly ascertain the average reliability and reliability boundaries of a circuit by using this approach for RCIVs identification. By applying optimizations of the identified RCIVs to expedite convergence and mitigate fluctuations, the influence of these RCIVs can be minimized in reliability evaluation and testing.
This paper develops a general framework for reliability assessment of multi-microgrid (MMG) distribution systems. It also investigates reliability impacts of coordinated outage management strategies ...in a MMG distribution network. According to the proposed reliability evaluation framework, which is based on sequential Monte Carlo simulation method, distribution system is divided into smaller sections/microgrids based on protection system configuration and operating measures are efficiently simulated considering different operation modes. In order to demonstrate the role of outage management strategy in reliability performance of MMG distribution systems, at first, the required features of an outage management strategy are identified. Then, suitable centralized and hierarchical schemes are introduced for operation of such systems during outage events. The proposed schemes, which are based on model predictive control approach, minimize total load curtailments in the system. Moreover, they are flexible and can effectively deal with multiple contingencies as well as uncertainties of outage duration. The developed reliability assessment framework is applied to a test system and performance of the presented outage management schemes are explored through extensive case studies. Obtained results suggest that implementation of an appropriate coordinated scheme is crucial to reliable operation of MMG distribution systems.
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