Reliability Analysis Of Systems Research Articles

A new approach for dynamic reliability analysis of reactor protection system for HPR1000

Traditional static methods are mostly adopted to analyze the reliability of reactor protection system (RPS) for nuclear power plant (NPP). However, they cannot characterize its dynamic interaction, time correlation, and uncertainty. In order to solve this problem, dynamic fault tree (DFT) analysis was firstly utilized to create the DFT for the RPS to characterize its dynamic interaction. Then, dynamic Bayesian network (DBN) was used to create the DBN model based upon the DFT for the RPS to characterize its dynamic interaction, time correlation, and uncertainty. Furthermore, Latin hypercube sampling (LHS) was employed to define a new Bayesian inference algorithm. Finally, the defined algorithm was applied in a RPS for Hualong-1 (HPR1000) in East China to conduct its dynamic prediction and sensitivity analyses through the Bayesian forward and backward inferences, and a new approach for dynamic reliability analysis of the RPS for HPR1000 was proposed. The research results showed that the proposed approach could be used for the dynamic reliability analysis of the RPS.

Efficient System Reliability Analysis for Layered Soil Slopes with Multiple Failure Modes Using Sequential Compounding Method

Evaluating the system reliability of layered soil slopes is a challenging issue because multiple failure modes may be included along the slip surfaces, which makes the overall failure probability greater than any individual slip surface. In this paper, an efficient system reliability analysis concerning the layered soil slopes is conducted based on the sequential compounding method (SCM) that has the ability to compound multiple failure events into an equivalent event sequentially. First, the first order reliability method (FORM) is employed to quantify initial reliability indices and correlation coefficients among these failure modes. Subsequently, the SCM is used to calculate the equivalent reliability indices and correlation coefficients until the multiple failure events are reduced to a compound event, and then the system reliability of the slope is obtained accordingly. The application of the approach to probabilistic evaluation of layered slopes is illustrated by two typical examples, and the correctness is verified by a Monte Carlo simulation (MCS). The results show that the SCM can deliver accurate system failure probability and greatly improve the computational efficiency compared with the MCS, which is an advantageous and promising strategy in evaluating the system reliability of layered soil slopes.

Research on Seismic Connectivity Reliability Analysis of Water Distribution System Based on CUDA

To improve the seismic connectivity reliability (SCR) analysis efficiency of water distribution systems (WDS) based on Monte Carlo (MC) simulation, the quasi-Monte Carlo (QMC) method sampled by a low-discrepancy sequence is applied. Furthermore, a parallel algorithm combined with the breadth-first search algorithm for SCR analysis of WDS based on the QMC method and Compute Unified Device Architecture (CUDA) platform was proposed. A city WDS was taken as a computational example, the accuracy and efficiency of the traditional MC algorithm and parallel algorithm were compared, and the influence of the Sobol sequence and pseudo-random number sequence was analysed. The analysis results show that when 1,000,000 simulations are performed, the maximum error of the calculation results of the two methods is 0.2%, and the parallel method can obtain a six-fold speedup ratio compared with the serial method, indicating that the proposed parallel method is correct, meets the accuracy requirements, and helps to improve the SCR analysis efficiency. When the number of simulations is the same, the simulation results based on the Sobol sequence are more accurate than those based on the pseudo-random number sequence. The proposed parallel method also achieves a good acceleration effect in the SCR analysis of large-scale WDS.

A reliability analysis technique for complex systems with retaining fuzzy information

AbstractThis paper proposed a method for the reliability analysis of systems characterized by fuzzy failure probabilities and intricate failure behaviors, while retaining the fuzzy information throughout the analysis process. Specifically, we introduced a combinational modeling method that integrates generalized stochastic Petri nets (GSPN) and dynamic fault trees (DFT) to capture the dynamic failure behaviors and address the limitations of DFT modeling. This combinational approach is capable of capturing more sophisticated failure behaviors, such as competing failures and global failure propagation patterns. Furthermore, we propose an Monte Carlo technique based on endpoint sampling to enable quantitative analysis of GSPN‐based composite models, which preserves the fuzzy fault information of the system. Finally, we demonstrate the effectiveness of the proposed method through an example of a flight control system.

Reliability analysis of the multi-state system with nonlinear degradation model under Markov environment

Research conducted on multi-state systems has garnered significant attention as a result of their engineering applications in the aerospace industry. The paper explores the intricacies of multi-state systems operating in complex environments and experiencing degeneration processes. A mixed shock model is introduced, which consists of multiple run shock models and extreme shock models. A state transition matrix is constructed using a Markov chain embedding method to establish a reliability model of hard failure process. In contrast to the traditional reliability modeling approach of hard failure process, this method incorporates time-dependencies. A numerical integration method is employed to derive the reliability function of the soft failure process based on the Gamma process. To validate the effectiveness of the developed methodology, an application of a micro-engine is utilized. Finally, compared with the Monte Carlo simulation, the developed model can be considered as a useful tool for the system manager to assess the reliability of the multi-state system with the nonlinear degradation process.

An adaptive sequential sampling method based on ANN_MCD and RF: Application in geotechnical problems

In geotechnical engineering, surrogate modeling is a vital technique for enhancing computational efficiency when faced with limited and costly samples. However, most adaptive sequential sampling (ASS) methods use the Kriging model, which directly evaluates the uncertainty of the model at unsampled points, but they are not efficient in solving multiple output problems. A few studies use cross-validation to indirectly evaluate the uncertainty of other surrogate models, which is time-consuming to repeatedly train multiple models. In addition, few methods consider input feature sensitivity, which further reduces the efficiency of ASS methods. To address these issues, this study proposes a hybrid ASS method that combines artificial neural networks with Monte Carlo dropout (ANN_MCD) and random forests (RF). The ANN is suitable for multiple output problems, and the MCD enables the ANN architecture to stochastically transform and efficiently predict uncertainty at unsampled points with a single model training. The RF evaluates input feature sensitivity, assigns weights to the sampling space based on feature sensitivity, and effectively avoids the interference of low-sensitivity features. Furthermore, two active learning functions are proposed to regulate the sampling areas on either a global scale or close to the limit state function, depending on the problem type. The study validates the efficacy of the proposed method through three representative geotechnical engineering examples: system reliability analysis of soil slope, back-analysis of soil constitutive parameters, and estimating the penetration rate in diamond drilling. The outcomes demonstrate that the proposed method has less computational cost than related studies and has more potential in addressing geotechnical sampling.

Insights from 665 tank farm fires—The second database update

AbstractThis article updates earlier publications of an ongoing research project on worldwide fire incidents that occurred in tank farms, which store products ranging from low flashpoint materials to crude and heavier hydrocarbons. The incidents and information in the database came from almost 300 sources, including technical reports and news releases. This research supports tank farm hazard identification/risk assessment and the reliability analysis of fire water protection systems (FWS). The research provides valuable insights such as the frequency of tank farm fires, the frequency of boilovers, the frequency of injuries/fatalities, the probability of failure of the FWSs, and the time to extinguish (TtEx) the fires. When the database provides sufficient information, these numbers appear as a function of the stored product. The data also allows plotting TtEx as a function of time for over a century. Additionally, this article illustrates the results of a confidence level analysis of the risk assessment of a distribution terminal that handles low and high‐flashpoint products.

Weighted low-discrepancy samplings: A novel method for slope system reliability analysis

This paper presents an efficient method for system reliability analysis of geotechnical examples based on the low-discrepancy sequences (LDS) sampling technique in combination with probabilistic weighting of the samples, i.e., the weighted low-discrepancy samplings (WLDS). In the framework of quasi-Monte Carlo simulation (QMCS), the Sobol’s quasi-random LDS are employed to generate the basic random variable samples that can significantly improve the efficiency of probabilistic weighting for the probability of failure calculation. In particular, the most probable system failure points (MPPs) can be directly captured according to the probabilistic weighting calculation. Via Nataf transformation, the WLDS computational framework incorporating correlated random variables in soils is also illustrated. Focusing on geotechnical applications, the reliability analyses of example soil slopes are presented to verify the performance of the proposed method. It is found that the proposed WLDS method not only can accurately obtain the probability of system failure but also can capture the multi-MPPs defining different earth slope failure modes in a cost-effective manner. The results in this study demonstrate that the proposed WLDS method is a feasible and promising method for performing the system reliability analysis of complex geotechnical earth structures.

A generalized system reliability model based on survival signature and multiple competing failure processes

Degradation-based system reliability analysis has been extensively conducted, but the components in a system are assumed to experience similar degradation and shock processes, neglecting actual failure mechanisms. However, multiple types of components in a system may work under different operational conditions and break down due to different failure mechanisms. Hence, a new generalized reliability model is proposed for systems with arbitrary structures experiencing multiple degradation and shock processes, including pure degradation processes (DPs), independent and dependent competing failure processes (CFPs). In this work, the Tweedie exponential-dispersion (TED) process is utilized to describe multiple degradation processes of the components, which contains the Wiener, Gamma, inverse Gaussian, and other processes as special cases. Based on multiple DPs and CFPs, a generalized reliability model is established by utilizing the structure analysis method, the survival signature, which allows the proposed method to be applied to various structural systems. Finally, an example of an automotive braking system with four types of components experiencing multiple DPs and CFPs is applied to illustrate the proposed model.

Reliability Design of Two-Stage Planetary Gear Wheel Hub Reducer based on Multi-Objective Optimization

The mining dump truck has a large load capacity, low running speed, and the characteristics of low speed and high torque. The planetary wheel reducer can meet this demand, which is widely used in such vehicles. According to the structural characteristics of a 170-ton vehicle two-stage planetary gear reducer, the reliability of planetary gear transmission at all levels was analysed, and the system parameters were optimized on this basis. The multi-objective optimization design method was used to transform the maximum reliability of the system into the maximum reliability of the gears at all levels, and the constraints are established based on the actual processing, operation, installation, assembly and other working conditions. The MATLAB multi-objective optimization module was used to solve the analysis, and the best gear design parameters were obtained after rounding. Under different operating conditions, the planetary reducer before and after optimization was compared and analysed to obtain the reliability and service life of the entire deceleration system after optimization. The analysis results show that: after the parameter optimization of the planetary reducer, the transmission efficiency is 0.946, and the transmission ratio of the system is 30.54, which meets the actual use requirements. The reliability coefficient of the contact strength of the tooth surface of the secondary sun gear is 2.52, the reliability coefficient of the bending fatigue strength of the root of the secondary inner ring gear is 2.59, and the reliability of the secondary gear is 0.989. The reliability is obviously improved. The safety factor of the main parts is high. The service life of each gear of the whole secondary reduction system meets the design requirements. The analysis content and results provide reference for the optimization design and reliability analysis of similar planetary gear systems.

Analysis of system reliability based on weakest t-norm arithmetic operations using Pythagorean fuzzy numbers

Analysis of system reliability based on weakest t-norm arithmetic operations using Pythagorean fuzzy numbers

Reliability analysis of complex multi-state system based on universal generating function and Bayesian Network

In the complex multi-state system (MSS), reliability analysis is an important research content, both for equipment design, manufacturing, operation and maintenance. Universal Generating Function (UGF) is an essential method in reliability analysis, which efficiently obtains system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are unclear or without explicit expressions, the UGF method is difficult to use or not applicable at all. Bayesian Network (BN) has a natural advantage in terms of reliability inference for the relationship without explicit expressions. When the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective shortcomings of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS. In the UGF-BN framework, the UGF method is first used to analyze the bottom components with a large number. Then probability distributions obtained are taken as the input of BN. Finally, the reliability of the complex MSS is modeled by the BN method. This proposed method improves the computational efficiency, especially for the MSS with a large number of bottom components. Besides, the aircraft reliability-based design optimization based on the UGF-BN method is further studied with budget constraints on mass, power, and cost. Finally, two cases are used to demonstrate and verify the proposed method.

A novel approach based on fault tree analysis and Bayesian network for multi-state reliability analysis of complex equipment systems

Due to the complex structure of multi-state complex systems and the lack of data, information, and knowledge, the uncertainty of the logical relationship between the failure states of systems and components and the uncertainty of related failure data become the key issues in the reliability analysis of multi-state complex systems. In this paper, combined with multi-state fault tree (MSFT), a multi-state reliability assessment framework for complex systems considering uncertainty based on multi-source information fusion and multi-state Bayesian network (MSBN) is proposed. The multi-source information fusion method combines historical data and experts’ opinions to solve the uncertainty problem of multi-state failure data in complex equipment systems effectively. Based on the multi-source information fusion method, the calculation method of multi-state prior probability and the construction method of conditional probability are given. By constructing the conditional probability table (CPT), the uncertain logic relationship between the multi-state nodes is effectively expressed, which effectively improves the efficiency of CPT acquisition for MSBN and reduces the workload of experts scoring. Finally, a mud circulating system is taken as an example to prove the proposed method, and the specific calculation process, evaluation results, and some discussions are given. The results show that the proposed method is an effective multi-state reliability analysis method for complex uncertain multi-state systems.

Hybrid Bayesian Networks for the Reliability Analysis of Systems with Continuous Variables

The standard way of dealing with continuous variablesinto reliability models is to discretize (or even binarise)them, resulting in discrete state models. The presentpaper proposes an approach where continuous systemvariables can be directly exploited by resorting to HybridBayesian Networks (HBN), where both continuousand discrete variables can be mixed in a general way.This allows one to: model the inter-dependencies betweendiscrete state components or subsystems, modelthe inter-dependencies between continuous system variables,model the influence of contextual information onsystem variables and components, model the definitionof specific system events or conditions given specificvalues of the system variables. We will show how theabove issues can be captured in a principled way bythe HBN formalism, by making the final analyses moregrounded on the actual values of every system variable.We finally present a case study where the model of agranule storage tank system of a petrochemical plant isconsidered, and we present the results of specific analysesimplemented as inference on the HBN model.

System reliability analysis for independent and nonidentical components based on survival signature

Survival signatures have been widely used for analyzing arbitrary system structures having independent failure events. It is challenging to study system reliability when diverse components are present since the fundamental premise of the survival signatures is that components should be interchangeable within the same subsystem. In this research, two methods based on the fundamental survival signature idea are suggested to examine the reliability of complex systems with independently but not necessarily identically distributed (INID) components. The first algorithm is based on the weighted random sampling (WRS) method to calculate the survival signatures. The second algorithm adopts the idea of divide-and-conquer in computing the probability structure. The application of these two algorithms and the exhaustive algorithm in analyzing the example systems are presented. The results show that the two algorithms can significantly reduce the computation time compared to the traditional methods. Finally, the developed algorithms are used in the reliability analysis of a real practical problem, the Tsinghua campus water supply pipeline system. The results regarding the predictions of system reliability as well as the component importance index are discussed.

Hybrid DFM – Petri net approach for dynamic reliability analysis of smart transmitters

AbstractSmart transmitters are increasingly being deployed in safety critical systems. Although the processing and diagnostic features provided by digital systems are advantageous, it introduces additional complexity in reliability modelling of systems. The conventional Fault Tree Analysis (FTA) method for system reliability analysis has its shortcomings in modelling digital systems, to incorporate its dynamic system interaction and failure modes in detail. This is because some of the failure modes may arise due to the dynamic interactions between the modules of the system. Dynamic Flowgraph Methodology (DFM) was identified as one of the optimistic approaches to handle these interactions. DFM approach combines multi‐valued logic modelling and analysis capabilities to handle systems consisting of components that have multiple degraded states and exhibit dynamic behaviour. However, only few DFM tools have been developed and some of them are not available in public domain. The DFM approach relies heavily on development and implementation of its decision tables. The logic information present within the decision tables can be meticulously implemented using Petri Nets. Also, there are plenty of free Petri Nets tools available online. Thus, the idea of DFM was used in this work to capture the dynamic interactions by developing decision tables. The decision tables were transformed into the Petri Net models to perform the analysis. The paper demonstrates the potential of the hybrid DFM – Petri Net approach for dynamic reliability analysis of smart transmitter.

Efficient model‐correction based reliability analysis of uncertain dynamical systems

AbstractIn this contribution, a model‐correction‐based strategy is applied for efficient reliability analysis of uncertain dynamical systems based on a low‐fidelity (LF) model whose outcomes are corrected in a probabilistic sense to represent the more realistic outcomes of the high‐fidelity (HF) model. In the model‐correction approach utilized, the LF model is calibrated to the HF model close to the so‐called most probable point (MPP) in standard normal space, which allows a more realistic assessment of the considered complex dynamical system. Since only few expensive limit state function evaluations of the HF model are required, an efficient reliability analysis is enabled. In an application example, the LF model describes an existing single span railway bridge modelled as simply supported Euler‐Bernoulli beam subjected to moving single forces representing the axle loads of a moving train. The HF modelling approach accounts for the bridge‐train interaction by modelling the passing train as mass‐spring‐damper (MSD) system, however increasing the computational effort of the limit state function evaluations. The failure probabilities evaluated with the model‐correction approach are contrasted and discussed with the failure probabilities of the sophisticated bridge‐train interaction model. It is shown that the model‐correction‐based approach provides reliable failure probability prediction of the HF model while leading to a significant reduction in computational effort.

Reliability analysis of a BWR plant system at startup stage - analysis by the GO-FLOW methodology with consideration of loop structures and phased mission problem -

A reliability analysis of a boiling water reactor (BWR) system at startup stage has been performed. This paper is a useful attempt at a reliability analysis of a phased mission system (PMS) with loop structures in a real engineering system. The system startup schedule has been set based on typical BWR system. During startup, phased mission problem occurs, and loop structures are formed in the system. The solution of Boolean expression for a reliability of loop structure has been explained. Reliability analysis has been performed by the GO-FLOW methodology; a brief explanation of the GO-FLOW has been given. The system has been modeled on a GO-FLOW chart. The loop structured parts have been modeled corresponding to the solution of the Boolean equations. The chart suggests a way to model logical loop relations in a diagram without loop structure. The PMS of a BWR system has been also modeled on the same GO-FLOW chart by using the phased mission operator. An exact analytical approach for loop structured system has been selected and both the logic of loop structures and the logic of PMS have been modeled on the same GO-FLOW chart. This allows PMS with loop structures to be easily solved. The mission success probabilities of the system have been obtained in a single computation by a single GO-FLOW chart. The proposed modeling method for logical loop structures and phased mission problem is useful in evaluating reliability and/or availability of large complex engineering systems.

Reliability analysis of cold-standby phased-mission system based on GO-FLOW methodology and the universal generating function

Reliability analysis of cold-standby phased-mission system based on GO-FLOW methodology and the universal generating function

Application of state‐space models with frequency‐dependent uncertainty for efficient reliability analysis of complex structures

AbstractIn this work, we introduce a commonly used framework to model uncertain dynamical systems and adapt it to propagate both parameters uncertainty and model mismatches through a dynamically excited railway bridge system. The approach considers the system inherent uncertainties in the frequency domain and leads to a significant reduction of the computational effort when performing an uncertainty quantification tasks. In an application example the proposed approach is utilized for efficient reliability analysis of the uncertain railway bridge system. The utilized technique allows for an efficient generation of structural responses, and thus enables a fast evaluation of failure probabilities by crude Monte Carlo sampling. Accuracy of the uncertain prediction model is ensured by comparing the failure probabilities with failure probability simulations based on a semi‐analytical Euler‐Bernoulli bridge beam model.