Reliability Analysis Of Systems Research Articles

Fuzzy system reliability analysis based on new aging indexes

The concept of aging plays an important role in reliability analysis. The aim of this paper is providing the novel method to definition aging indexes based on fuzzy random variables and using the concept of α-pessimistic in both parametric and non-parametric approaches. In parametric approach, the concepts of survival function, hazard rate function and mean residual function were extended for fuzzy random variables. After that, we obtain and analyse these indexes for fuzzy exponential and fuzzy two-parameter Weibull distributions as two frequently used lifetime distribution in reliability theory. Moreover, with the assumption that the distribution of fuzzy observations is unknown (non-parametric approach), first an estimate for the fuzzy distribution function is presented, and then an estimate for the aging indices of the system is investigated. To do this, the concept of fuzzy empirical distribution function is investigated as an estimator of cumulative distribution function. Moreover, some properties of investigated estimator have been established.

Reliability analysis for manufacturing system of drive shaft based on dynamic Bayesian network

Reliability analysis for manufacturing system of drive shaft based on dynamic Bayesian network

Reliability analysis of a grid-connected hybrid renewable energy system using hybrid Monte-Carlo and Newton Raphson methods

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

Reliability and availability analysis of high-altitude platform stations through semi-Markov modeling

In the development and deployment of both tethered and autonomous High Altitude Platform Station (HAPS), flight modules such as hexacopter and octocopter with 6 or 8 rotors are most often used. The primary focus of this article is to compute the reliability of tethered HAPS by analyzing the effectiveness of their redundant rotor systems. To measure the performance of such systems, it is convenient to consider mathematical models as k-out-of-n models. Although Markov models have extensively been reported to analyze the reliability of repairable k-out-of-n models, this work introduces an alternative and more thorough methodology. In this work, a semi-Markov model (SMM) is proposed to calculate the probability of rotors being operational after the failure of some rotors in order to analyze system reliability. We emphasize that an SMM model is more appropriate as it provides a more accurate assessment of the probability of system failure over time through non-exponential sojourn time. Further, the efficacy of the proposed SMM is studied through the evaluation of steady-state availability, mean time to failure and reliability. Overall, the findings of this study enhance our comprehension of the reliability of repairable systems and extend the existing knowledge base of HAPS reliability analysis.

Intuitionistic fuzzy approach for reliability analysis of NSP system under time varying failure rates

Intuitionistic fuzzy approach for reliability analysis of NSP system under time varying failure rates

Hierarchical Bayesian modeling for calibration and uncertainty quantification of constitutive material models: Application to a uniaxial steel material model

Constitutive material models play an integral role in finite element (FE) modeling of structural components and systems and are governed by a set of parameters. The values of the material model parameters are important for the FE model to be able to predict the real structural behavior as closely as possible. Realistic cyclic material constitutive models well calibrated to experimental data are critical when predicting the nonlinear dynamic response of structural systems to extreme loads (e.g., earthquakes, high winds, blast). Hence, this study focuses on the inference and uncertainty quantification of material model parameters from an experimental dataset consisting of data from experiments on multiple specimens of the same kind, which is a topic of great interest to the structural modeling and simulation community. Traditionally, Bayesian inference is performed by pooling the data from all the specimens, or by considering the data from a single specimen, disregarding the inherent variability (aleatory uncertainty) among specimens of the same kind. Consequently, traditional approaches fail to capture the population distribution of material model parameters, which represents the aleatory specimen-to-specimen variability that is essential for conducting reliability analysis and uncertainty propagation studies. Moreover, these approaches cannot accurately simulate the response of new specimens. To overcome these limitations, this study introduces a novel application of hierarchical Bayesian inference for calibrating material model parameters. By simultaneously considering experimental data from multiple specimens of the same kind, hierarchical Bayesian modeling quantifies both epistemic uncertainty and aleatory specimen-to-specimen variability. In this study, the parameters of the well-known Giuffré-Menegotto-Pinto (GMP) uniaxial material model for reinforcing steel are calibrated and validated based on a dataset of experimental cyclic tests conducted on thirty-six steel specimens, or coupons, originating from three different manufacturing mills, complying to two different manufacturing standards, and subjected to different strain histories. These strain histories are similar to those experienced during seismic events by reinforcing steel bars within reinforced concrete structures. The material model parameters are calibrated using both the traditional and the hierarchical Bayesian approach and the results are compared. Based on the experimental dataset employed in this study, a joint probability distribution of the GMP material model parameters that quantifies both epistemic (due to finite sample size) and aleatory (coupon-to-coupon) variability or uncertainty is provided which can be used for uncertainty propagation in reliability and risk analyses of nonlinear dynamic systems.

Reliability analysis of multi-component systems subjected to dependent degradation processes and random shocks in dynamic environments

This article presents reliability models for multi-component systems subjected to multiple internally dependent degradation processes and external shocks in dynamic environments. We use the homogeneous semi-Markov process (HSMP) to describe the evolution of the environment state and the non-homogeneous Poisson process (NHPP) to model the arrival of shocks. The degradation processes of the system components are modeled using either general path models (GPM) or stochastic processes. The models highlight the shock-induced degradation acceleration, the degradation-shock dependence, and the influence of dynamic environments on the degradation processes and shock damage. The integrated process evolution (i.e., system degradations, shock arrivals, and environment shifts) follows a non-homogeneous Markov renewal process (NHMRP) in a multidimensional space. The NHMRP semi-Markov kernel and theoretical formulation for system reliability are derived. We provide a simulation algorithm to estimate system reliability. Finally, we demonstrate the theoretical correctness and applicability of the model using a micro-electro-mechanical system (MEMS).

A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system

A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system

Performance and Reliability Analysis for PBFT-Based Blockchain Systems With Repairable Voting Nodes

Performance and Reliability Analysis for PBFT-Based Blockchain Systems With Repairable Voting Nodes

An efficient system reliability analysis method for flap mechanism under random-interval hybrid uncertainties

An efficient system reliability analysis method for flap mechanism under random-interval hybrid uncertainties

Integrating reliability analysis into MBSE for FPGA-based safety critical I&C system design in nuclear power plants

Abstract With the widespread application of Field Programmable Gate Array (FPGA) technology in the instrumentation and control (I&C) systems of nuclear power plants (NPPs), its design reliability has become the key to ensuring the subsequent safe operation of NPPs. However, the traditional document-based design method is no longer sufficient to meet the current design needs of complex systems, and there is a lack of research related to the reliability and security analysis of FPGA-based systems. Therefore, the overall goal of this study is to integrate reliability analysis into the model-based systems engineering (MBSE)-based design of FPGA-based I&C systems. Based on the typical application characteristics of the FPGA, a digital design framework for FPGA-based systems is proposed to realize a traceable design process from requirements to implementation. Subsequently, a model-based forward comprehensive reliability analysis method is proposed, which uses a SysML-based mapping mechanism to integrate Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods into the design process, so as to comprehensively identify and analyze failure modes from systems to components and optimize and improve system design. Finally, the proposed method is applied to the analog output design of FPGA-based systems. The proposed method can provide guidance for improving the reliability of FPGA-based DI&C systems and provide theoretical basis for the further engineering application.

Joint optimization of preventive maintenance and triggering mechanism for k-out-of-n: F systems with protective devices based on periodic inspection

Systems with protective devices (PDs) have attracted increasing research interests due to their extensive engineering applications. In practice, the PDs are triggered to function under certain conditions. However, existing studies focus on the reliability modelling and analysis for such systems, and the joint optimization of maintenance and PD's triggering mechanism has not been investigated. Consequently, this paper explores the joint optimization of maintenance and triggering mechanism for k-out-of-n: F systems with PDs. A new triggering mechanism of the PD is proposed based on the number of failed or unhealthy components. The working PD can reduce the impacts of shocks on components. Based on the detected system status through periodic inspections, corresponding actions including corrective maintenance for components or the PD, preventive maintenance for components or shutting down the PD can be decided to improve the system reliability. The operation process of the established system is modelled by Markov decision process, and optimal actions under diverse system states are obtained via value iteration algorithm. The optimal inspection interval and triggering mechanism are derived by solving the optimization model aiming to minimize the expected total cost. Finally, the applicability of the proposed model is validated by abundant numerical examples.

Reliability analysis of digital reactor protection systems in floating nuclear power plants

Abstract This paper presents a reliability model for digital reactor protection systems (RPSs) in floating nuclear power plants (FNPPs) that accounts for both the internal characteristics of RPS and the external environment. The internal characteristics of RPS include independent failures and common-cause failures (CCFs) of components, repair behavior, and actuation logic degradation. For the external environment, we incorporated a parts-pressure method and used the environmental factors to describe the impact of marine environment at component level. Detailed Monte Carlo simulation (MCS) algorithm was proposed to solve the reliability models with different environmental factors, and the results showed that the maximum value of the environmental factor was 3.2 under the requirements that the probability for RPS failing to generate the trip signal does not exceed 1 × 10−5 and the spurious trip frequency does not exceed one time per year. Reliability indexes, such as failure probability and spurious trip frequency, were also derived. The 90 % confidence intervals of these two indexes were further calculated in the uncertainty analysis by using the kernel density estimation (KDE) approach.

Application of picture fuzzy set theory to reliability and risk analysis of complex industrial systems

Application of picture fuzzy set theory to reliability and risk analysis of complex industrial systems

System reliability analysis of geogrid reinforced retaining wall using random finite element method

Stabilizing earthwalls is extremely important in geotechnical engineering projects and has become an integral part of transportation infrastructures. Meanwhile, geogrid reinforced retaining walls have been the attention of designers due to their advantages. On the other hand, the soil heterogeneity and the requirement of investigating the internal (geogrid rupture and geogrid pullout) and external (global and lateral displacement) stability modes of these structures have necessitated performing system reliability analysis. In this study, finite element in conjunction with random fields is used to evaluate the reliability of these walls. For this purpose, a finite element program is coded in MATLAB to obtain internal and external safety factors considering staged construction. The deterministic program is extended to a stochastic framework to account for spatial variability of soil parameters in retained backfill, foundation soil, and reinforced fill. In the last part of this study, reliability indices of stability modes are calculated to obtain the series system reliability index, utilizing the Sequential Compounding Method (SCM). The results indicated that the effect of soil heterogeneity is more significant on internal stability modes compared to external stability modes especially geogrid pullout. The results of the system reliability analysis demonstrated more critical conditions thus the reliability index of the system was less than the reliability index of individual components.

A Curved Surface Integral Method for Reliability Analysis of Multiple Failure Modes System with Non-Overlapping Failure Domains

Abstract In the study of reliability of systems with multiple failure modes, approximations can be obtained by calculating the probability of failure for each state function. The first-order reliability method and the second-order reliability method are effective, but they may introduce significant errors when dealing with certain nonlinear situations. Simulation methods such as line sampling method and response surface method can solve implicit function problems, but the large amount of calculation results in low efficiency. The curved surface integral method (CSI) has good accuracy in dealing with nonlinear problems. Therefore, a system reliability analysis method (CSIMMS) is proposed on the basis of CSI for solving multiple failure modes system reliability problems with non-overlapping failure domains. The order of magnitude of the failure probability is evaluated based on the reliability index and the degree of nonlinearity, ignoring the influence of low order of magnitude failure modes, and reducing the calculation of the system failure probability. Finally, CSIMMS and other methods are compared by three numerical examples, and the results show the stability and accuracy of the proposed method.

Multi-fidelity wavelet neural operator surrogate model for time-independent and time-dependent reliability analysis

Operator learning frameworks have recently emerged as an effective scientific machine learning tool for learning complex nonlinear operators of differential equations. Since neural operators learn an infinite-dimensional functional mapping, it is useful in applications requiring rapid prediction of solutions for a wide range of input functions. A task of a similar nature arises in many applications of uncertainty quantification, including reliability estimation and design under uncertainty, each of which demands thousands of samples subjected to a wide range of possible input conditions, an aspect to which neural operators are specialized. Although the neural operators are capable of learning complex nonlinear solution operators, they require an extensive amount of data for successful training. Unlike the applications in computer vision, the computational complexity of the numerical simulations and the cost of physical experiments contributing to the synthetic and real training data compromise the performance of the trained neural operator model, thereby directly impacting the accuracy of uncertainty quantification results. We aim to alleviate the data bottleneck by using multi-fidelity learning in neural operators, where a neural operator is trained by using a large amount of inexpensive low-fidelity data along with a small amount of expensive high-fidelity data. We propose the multi-fidelity wavelet neural operator, capable of learning solution operators from a multi-fidelity dataset, for efficient and effective data-driven reliability analysis of dynamical systems. We illustrate the performance of the proposed framework on bi-fidelity data simulated on coarse and refined grids for spatial and spatiotemporal systems.

A novel LSTM-integrated non-intrusive ROM for reliability analysis of hysteretic systems with large stochastic dimension

Failure probability estimation of hysteretic systems subjected to random process excitation appears in many practical engineering applications, such as structures subjected to earthquake and vibration control. This task is computationally challenging due to the need for repeated solutions of a high-resolution model. This cost grows significantly with the stochastic dimension — characterized by the number of random variables, in this case, arising from the discretization of the random excitation. To address this issue, a novel non-intrusive reduced order model (ROM) is proposed in this paper. Accordingly, the complexity due to spatial discretization is first reduced using proper orthogonal decomposition. However, interpolation among response time histories in the resulting reduced solution space is computationally infeasible due to large stochastic dimensionality. An existing ROM developed by the authors is now improved by adopting a long short-term memory (LSTM) network for this interpolation. Furthermore, this adoption required two additional modifications in the mathematical structure of the ROM related to data compression and nonlinear coupling. The proposed methodology can be seamlessly integrated with black-box nonlinear solvers. Through detailed numerical studies on a beam on Winkler foundation and a multi-storied building frame subjected to stationary and non-stationary excitations, the proposed ROM is found to be accurate, efficient, and robust with respect to the frequency band of interest. The ROM has shown reduction of two orders of magnitude in the computational time. Furthermore, the proposed ROM is inherently parallelizable and holds promise to be scaled to other types of nonlinear systems with large stochastic dimensionality.

Reliability analysis of multiple repairable systems under imperfect repair and unobserved heterogeneity

AbstractImperfect repairs (IRs) are widely applicable in reliability engineering since most equipment is not completely replaced after failure. In this sense, it is necessary to develop methodologies that can describe failure processes and predict the reliability of systems under this type of repair. One of the challenges in this context is to establish reliability models for multiple repairable systems considering unobserved heterogeneity associated with systems failure times and their failure intensity after performing IRs. Thus, in this work, frailty models are proposed to identify unobserved heterogeneity in these failure processes. In this context, we consider the arithmetic reduction of age (ARA) and arithmetic reduction of intensity (ARI) classes of IR models, with constant repair efficiency and a power‐law process distribution to model failure times and a univariate Gamma distributed frailty by all systems failure times. Classical inferential methods are used to estimate the parameters and reliability predictors of systems under IRs. An extensive simulation study is carried out under different scenarios to investigate the suitability of the models and the asymptotic consistency and efficiency properties of the maximum likelihood estimators. Finally, we illustrate the practical relevance of the proposed models on two real data sets.

The Reliability and Availability Analysis of a Single-Unit System under the Influence of Random Shocks and the Variation in Demand from Production with Erlang Distribution

This study evaluates the reliability measures for a system consisting of a single unit that is subject to random shocks at random times with mismatches between demand and production. The system may sometimes be subject to random shocks leading to a system failure with probability p. The system may also fail completely for various reasons other than shocks. The unit is serviced by a technician if it is affected by a shock. However, the system may fail during operation for various other reasons, such as a programming error, human error, operational continuity and stress, and weather conditions. A single technician immediately repairs or maintains the failed unit. The system is in an operational state when there is demand or in an idle state when there is no demand. The distributions of all times in this system follow a negative exponential, while the time to repair follows a two-stage Erlang distribution. The expressions for the reliability metrics are obtained as functions of time using the Laplace transform and the supplementary variables technique. Sensitivity and relative sensitivity analyses were also performed for the system parameters. Finally, the efficiency of the system is illustrated using numerical examples.