Reliability Analysis Of Systems Research Articles

Reliability analysis of multi-state balanced systems with standby components switching mechanism

An increasing attention has been paid to the reliability analysis of balanced system. In previous studies, there were three rebalancing strategies for balanced systems: forcing down the unbalanced pair of subsystems, restarting components that have been shut down, and dynamically adjusting the state of the working components. In this paper, a new rebalancing strategy is proposed by switching the standby components for a multi-state balanced system. The system consists of m subsystems, each with one working component and n-1 standby components. The system is balanced when the working components do not fail and the system's balanced degree doesn't exceed a threshold. Two cases of system balance conditions are considered: one is based on component states and the other is based on symmetric component states. If the system is imbalanced or fails, the system needs to adjust the state of the working component by switching switches to let the system operate normally. The switching rules for each case are given separately. In this paper, the finite Markov chain imbedding approach (FMCIA) is used to derive the system reliability. Finally, numerical examples and sensitivity analysis are given.

A FENN-TL Approach for Reliability Analysis of a Primary Ice Detection System

A FENN-TL Approach for Reliability Analysis of a Primary Ice Detection System

Reliability analysis of a redundant series system with common cause failures and delayed vacation

Reliability analysis of a redundant series system with common cause failures and delayed vacation

Novel Kriging based learning function for system reliability analysis with correlated failure modes

Novel Kriging based learning function for system reliability analysis with correlated failure modes

Optimal power dispatch capability and reliability analysis of hybrid renewable energy system

These days, using solar and wind energy resources is much more cost-effective and appealing. The rapid depletion and growing expense of non-renewable energy supplies resulted in the development of alternative energy sources that utilize renewable energy sources. In recent times, hybrid renewable energy algorithms have often been connected to wind and solar power systems. The primary objective of this work (PWBHPS) is to determine the power dispatch capacity of a PV-wind-battery hybrid power system. In the modern electric power market, the PWBHPS's power dispatch capacity needs to be explicitly specified at the beginning of every dispatching time interval in order to collaborate with the transmission system operator (TSO). Energy storage is a workable option for efficiently integrating renewable energy sources into the system. A recently developed storage device called the vanadium redox flow battery (VRFB) may have certain advantages in large-scale grid-connected applications. State-of-charge monitoring provides extra insights into battery performance by displaying increased capacity utilization and longer cycle times, both of which extend the life of the cells. This study looks at the minimum-max technique of individual solar energy, wind energy, and a combined PV wind battery hybrid power system to size a VRB battery for application in renewable energy systems. Individual wind and solar systems need larger battery capacities for consistent, constant power output; hybrid systems require batteries with lower ratings. An optimal charging mechanism is proposed and explored through MATLAB simulations.

A multi-fidelity stochastic simulation scheme for estimation of small failure probabilities

Computing small failure probabilities is often of interest in the reliability analysis of engineering systems. However, this task can be computationally demanding since many evaluations of expensive high-fidelity models are often required. To address this, a multi-fidelity approach is proposed in this work within the setting of stratified sampling. The overall idea is to reduce the required number of high-fidelity model runs by integrating the information provided by different levels of model fidelity while maintaining accuracy in estimating the failure probabilities. More specifically, strata-wise multi-fidelity models are established based on Gaussian process models to efficiently predict the high-fidelity response and the system collapse from the low-fidelity response. Due to the reduced computational cost of the low-fidelity models, the multi-fidelity approach can achieve a significant speedup in estimating small failure probabilities associated with high-fidelity models. The effectiveness and efficiency of the proposed multi-fidelity stochastic simulation scheme are validated through an application to a two-story two-bay steel building under extreme winds.

Security and dependability analysis of blockchain systems in partially synchronous networks with Byzantine faults

Blockchains enhance trust in decentralized systems through components like distributed consensus, peer-to-peer communication, and trustless computing. Deployed in real networks, they must ensure security and dependability at each level. This paper introduces security and dependability concepts in three key layers: consensus protocols, network infrastructures, and smart contract applications, derived from distributed systems theory and adapted to blockchains. It evaluates these attributes across eight famous blockchains, assuming realistic network deployments and Byzantine fault scenarios to reveal system strengths and weaknesses. The aim is to establish a comprehensive framework for blockchain assessments, providing insight into design choices and potential security issues.

Decomposed-coordinated framework with intelligent extremum network for operational reliability analysis of complex system

The analysis of operational reliability in complex systems, which involve numerous subsystems and multiple disciplines, presents significant computational challenges due to their highly nonlinear, transient nature and the presence of many hyperparameters. Although reliability analysis models have made progress, they are still inadequate for accurately modeling composite functions with multiple sublayers and sub-functions. To improve the performance of modeling composite functions, the decomposed-coordinated intelligent extremum network model (DC-IENM) is proposed in this paper. The present study employs the decomposed-coordinated (DC) strategy as a means to effectively address the coordination relationship among multiple analysis objectives. To assess the efficacy of the proposed approach, two illustrative examples are considered: (1) the approximate and probabilistic analysis of a nonlinear function with multiple responses, and (2) the reliability analysis of civil aircraft brake system temperature. These examples serve to demonstrate the effectiveness of the developed DC-IENM. Furthermore, the modeling and simulation properties are rigorously examined by means of a comparative analysis involving various methodologies. The obtained results unequivocally indicate that the proposed DC-IENM exhibits distinct advantages in terms of both computational efficiency and precision.

Reliability Analysis of a Three-Engine Simultaneous Pouring Control System Based on Bayesian Networks Combined with FMEA and FTA

Pouring is an important process in the production of solid propellant rocket engines, and usually, the cost of a solid propellant rocket engine is extremely high. Therefore, pouring production with high reliability is very important. The pouring of three engines of solid propellant rocket engines simultaneously can greatly improve its production efficiency. However, it makes the system more complex and redundant. For a multi-state system, it is difficult to make an accurate evaluation of system reliability. Aiming at the redundancy of multiple engines and acousto-optic combined control in the three-engine simultaneously slurry pouring alarm control system with dissimilar redundant alarm units, a reliability analysis method is proposed based on the combination of Failure Mode Effect Analysis (FMEA) and Fault Tree Analysis (FTA). The control system is divided into several redundant states according to the alarm function, and then the Bayesian Networks method is used for reliability evaluation and calculation. Finally, the reliabilities of systems with dissimilar redundancy degrees are obtained. The tangible results of this research work are as follows: (1) The research results obtained by applying the FMEA method laid a foundation for the establishment of a fault trees model for analyzing the reliability of the control system using the FTA method, in addition, which can guide the maintenance and fault identification of the control system during engineering application. (2) The calculated value of the reliability of the control system is 0.999989, and the mean time between failures is MTBF is 5 × 104 by using the fault tree analysis method, which proves that the designed three-engine simultaneous pouring system is very reliable. (3) Based on the calculation and comparison of the Bayesian Networks of redundant three-engine pouring control systems, the circuit diagram of the improved control system is identified.

Temporal noisy-adder of bayesian network for scalable consecutive-k-out-of-n:F system reliability analysis

The Bayesian network(BN) is a powerful tool for modeling and analyzing reliability, safety, and risk in various engineering systems. Although there have been studies that incorporate it into the modeling of Consecutive-k-out-of-n: F systems(C(k,n:F)), these applications are limited to naive mode and cannot tackle scalable systems (e.g., n>2000,k>40). By introducing temporal Noisy-adder structure, named NaCkoon model, the number of nodes in equivalent C(k,n:F) BN model is significantly reduced, and the Conditional Probability Table (CPT) of temporary nodes can be controlled to linear complexity. The correctness of the proposed algorithm is validated by the comparison of literature case. Through numerical experiments, the novel algorithms proposed in this paper were compared with existing ones, including BN construction and inference computation time, was found to be the most optimal solution. Finally, the computable ranges of novel and existing algorithms are obtained through the capability computation. The test results show that NaCkoon model proposed in this paper has great advantages over existing algorithms and proposed NaA+NaO model based BN.

Analyzing system reliability using conflicting bifuzzy failure rates of the components

Since uncertainty and ambiguity are common in real-world problems, numerous methods based on different kinds of fuzzy sets have been widely used in the evaluation of system reliability over the years. However, these methods are unable to deal with conflict uncertainty, particularly when it comes to the uncertain failure rates of the system's components. This paper introduces the idea of the triangular conflicting bifuzzy set (TCBFS), which is a novel paradigm for dealing with conflicting uncertainty. A novel method is devised to determine the membership and non-membership functions for the system's reliability when subsystems/components exhibit conflicting bifuzzy failure rates. In order to generate the membership and non-membership functions for various system configurations, including series, parallel, series-parallel, and parallel-series systems, non-linear programming is used to derive a number of functions based on conflicting bifuzzy numbers. The validity of the suggested method in modeling and evaluating system reliability with different conflicting bifuzzy failure rates is demonstrated through illustrated scenarios. This novel approach improves the precision and resilience of reliability estimations for systems by providing a more precise representation of uncertainties.

Time-dependent system reliability analysis for mechanical on-load tap-changer with multiple failure modes

To regulate voltage level, the mechanical on-load tap-changer is a crucial series system of the ultra-high-voltage converter transformer. Whether its motion is reliable throughout the whole process is vital for the safe operation of converter transformer. Therefore, the time-dependent system reliability analysis is carried on for the mechanical on-load tap-changer with multiple failure modes based on the first-passage method. First, the failure modes of the mechanical on-load tap-changer are comprehensively analyzed according to the safety requirement, and then, the output error of each failure mode related to the moving mechanism of the system is modeled based on the motion equation to establish the varied limit-state functions of the time intervals over the whole motion. Then, the first-passage method is leveraged to solve this time-dependent reliability problem with multiple failure modes, in which the analytical formula of the outcrossing rate, the crucial element of the first-passage method, is derived by introducing the first-order moment of the doubly truncated multivariate Gaussian distribution. On this basis, the time-dependent system kinematic reliability is calculated using the Simpson integration algorithm. Finally, a numerical case is studied first, and in what follows, the practical engineering application to the mechanical on-load tap-changer is showcased in detail to demonstrate the effectiveness and efficiency of the proposed method by comparison with previous methods.

Multivariate degradation system reliability analysis with multiple sources of uncertainty

As the industrial Internet of Things evolves, multi-sensor monitoring becomes increasingly prevalent, resulting in degrading systems frequently exhibiting multivariate degradation characteristics. Multiple performance characteristics are frequently interdependent due to the consistency of the operating environment, operating conditions, and internal structure of the system. On the other hand, performance characteristic detection is also affected by numerous uncertainties. A multivariate stochastic degradation model that takes dependencies with multiple sources of uncertainty into account is developed for the analysis of multivariately dependent degradation systems. To represent linear or nonlinear degradation qualities, the marginal degradation process is referred to as a generalized stochastic degradation model. By using the D-vine copula function and pair-copula construction, dependencies between numerous variables are formed. The marginal and joint distribution parameters of the degradation model are derived using a two-step parameter estimation procedure. Lastly, the C-MAPSS dataset is used to analyze the applicability of the proposed model for bivariate and multivariate instances. The effectiveness of the proposed method is verified by experiments.

Reliability Analysis of CTCS-3 Train-Ground Communication System Based on 5G-R

The reliability of Chinese Train Control System Level 3 (CTCS-3) train-ground communication system is of great importance to ensure the safe operation of trains. The fifth Generation Mobile Communication for Railway (5G-R) is prospective for train-ground communication in the future. In this paper, the train-ground communication procedure of 5G-R (e.g., connection reestablishment, handover and train-ground data transmission) is tested by a semi-physical simulation platform. The test results, including the delay of different procedures together with the handover success rate, all can satisfy the quality of service (QoS) requirements. According to the communication procedure, deterministic and stochastic Petri net (DSPN) models of communication links and train-ground communication scenarios are established. Furthermore, the parameters of models are calibrated with the test data and protocol standards. We utilize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula> -tool to simulate and analyze the reliability of the train-ground communication system under the interaction of multiple fault factors. The results show that even in the worst handover scenario where the station distance is 1 km and the train speed is 400 km/h, the reliability of the train-ground communication system based on 5G-R can still reach 99.9963 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . It not only meets the requirements of the train control services for the train-ground communication system, but also is better than the reliability of the GSM-R based communication system.

Extreme value analysis of high-dimensional Gaussian vector processes

The analysis of the extreme value pertaining to first-passage failure is a key consideration for engineering systems undergoing random vibration. Most of the literature focus on a univariate stochastic process, however for system reliability analysis, the extreme value of multiple dependent processes is required. Due to the problem complexity, analytical methods are scarce, and they are limited to low-dimensional problems (dimension refers to the number of component processes). This paper presents an analytical method for extreme analysis of multivariate stationary Gaussian processes. The method can efficiently solve high-dimensional problems, thus it has diverse applications such as analysing dynamic systems with many degrees-of-freedom, or evaluating space-time extremes of random fields by discretizing the spatial domain. A maximum process is defined, representing the maxima of the components at each time instant. The exact upcrossing rate of the maximum process is derived, and the Poisson approximation is invoked to estimate the extreme value distribution. The method is fast as it involves multiple one-dimensional integrals, which can be computed using Gaussian quadrature. Moreover, an accurate approximation is proposed that obviates the need for numerical integration. The proposed method is implemented on two examples, specifically a dynamic system and spreading ocean waves. Monte Carlo simulations, which are computationally demanding, are performed to verify the accuracy and correctness of the proposed method.

Reliability Analysis of a Building Real Fire Simulation Training System

Real fire simulation training systems have gradually become an important method of emergency rescue team training and improvement. However, the failure rate of such systems is high, which threatens the safety of the trainers. Therefore, this study takes a real fire simulation training scenario as the research object and analyzes the system structure of the real fire simulation training base. The system structure of the real fire simulation training base is analyzed and divided into three systems: a smoke and heat training room, a combustion training room, and a water, oil, and gas supply. Then, a reliability model is established, and the reliability is determined. The main structures affecting the reliability of the system are identified, and an optimization plan for improving the structure is proposed. The results show that the combustion training room is the least reliable of the three parts in the real fire simulation training base. The series link in the system structure should be reduced as much as possible to meet the training requirements while the parallel link should be increased, and a reserve system should be added if necessary.

A novel Bayesian-inference-based method for global sensitivity analysis of system reliability with multiple failure modes

Global reliability sensitivity analysis aims at quantifying the effects of each random source on failure probability or reliability over their whole distribution range and is highly concerned in reliability design and uncertainty control. And in practice, a structure or product usually has more than one component impacting their performance safety, which is essentially a system reliability problem. Therefore, this paper proposes a novel Bayesian-inference-based method for moment-based global sensitivity analysis of system reliability with multiple failure modes. First, the limit-state function of each component involved in the system is linearly approximated based on the reliability index. Then, the global reliability sensitivity is transformed into a problem of multivariable Gaussian probability within a given safe region where the dimension number is double of the failure modes. In this case, the Bayesian-inference-driven expectation propagation technique is introduced to solve this intractable problem in an analytical manner, based on which the closed-form solution to the global reliability sensitivity for system with multiple components is accordingly derived. Finally, a numerical case, a vehicle subjected to impact, a cantilever beam and a practical engineering application to a four-panel spaceborne deployable plane antenna are studied to demonstrate the effectiveness of the proposed method by comparison with Monte Carlo simulation.

Reliability Analysis of Nuclear Power Plant Electrical System Considering Common Cause Failure Based on GO-FLOW

The reliability of nuclear power plant electrical systems is an important guarantee of nuclear safety, and the common fault failure problem arising from redundant design and intelligent control may greatly affect reliability assessment results. Combined with the features of repairability, multi-state characteristics, and common fault failure of nuclear power plant electrical systems, a reliability analysis method of nuclear power plant electrical systems based on the GO-FLOW method considering common fault failure is proposed. This study firstly constructs the algorithmic model of combining operators of repairable components and the equivalent model of reliability parameters of multi-mode repairable components, then establishes a probability calculation model of common fault failure for repairable systems by considering the quantitative computation of the common signaling system model, and finally, quantitatively calculates the reliability of nuclear power plant electrical systems and their influencing factors. The example simulation calculates the reliability of the external power supply system and the electrical system of the nuclear power plant, analyzes the influence of the common signal processing and the common fault failure factors on the reliability of the electrical system of the nuclear power plant, and verifies the validity of the proposed method. The results show that the common fault failure factors have a large impact on the system reliability analysis; the common fault failure of the standby diesel generator set will seriously reduce the reliability of the electrical system, which can be improved by installing additional standby diesel generators.

A Novel Multi-state Bogie System Reliability Evaluation Approach by Extended d-MC Model for High Speed Train

Abstract Bogie is a pivotal system and plays a critical part in the safety and reliability management of high speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to a low efficiency. In order to overcome the mentioned drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d-MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d-MC model is established to remove unnecessary d-MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and the examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to testify the practicability of the method projected in this article, and is is found that the proposed method outperforms a newly developed method in solving the multi-state reliability problems.

System reliability analysis of joint rock slopes with the stepped sliding surface considering hydraulic and seismic actions

System reliability analysis of joint rock slopes with the stepped sliding surface considering hydraulic and seismic actions