Joint Failure Probability Research Articles

Comparative seismic analysis of frames with shape memory alloy slip friction dampers

This study investigated the seismic performance of 6-, 9-, and 12-story concentrically braced steel frames utilizing shape memory alloy slip friction dampers (SMASFDFs). For comparison purposes, identical frames equipped with self-centering braces (SCBFs) and buckling-restrained braces (BRBFs) were also designed and analyzed. The seismic performance of considered frames was evaluated through nonlinear static and nonlinear time history analysis (NLTHA) under three seismic hazard levels, i.e., frequently occurred earthquakes (FOE), design basis earthquakes (DBE), and maximum considered earthquakes (MCE). Incremental dynamic analysis (IDA) was conducted to examine the seismic responses, including peak interstory drift ratios (PID), peak floor accelerations (PFA), and residual interstory drift ratios (RID), of the designed frames under varying levels of seismic intensity. Fragility curves considering single and multiple seismic responses were obtained based on the IDA results. The results show that the SMASFDFs have comparable deformation control capacity as the BRBFs while exhibiting zero RID and more uniform deformation distribution over the building height. Moreover, in most circumstances, the joint failure probability is lowest for the SMASFDFs when considering multiple seismic responses. From a seismic design perspective, current results suggest that the SMASFDFs can be designed with the same strength reduction factor (R) if the PID is especially concerned.

Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis

Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 102 to 5.68 × 103) than estimates that assume coincident failures are the result of independent hydrologic events.

Improved seismic risk evaluation for high‐voltage switchgear equipment: A copula‐based framework considering joint failure modes

AbstractTraditional seismic evaluations for high‐voltage switchgear equipment, the critical power facilities, consider only structural failure mode and may underestimate the actual seismic risk. By additionally introducing a probable functional failure mode caused by the excessive deformation of the switchable blade of equipment, a novel copula‐based framework is proposed to utilize limited samples to generate simulation data and evaluate the joint failure probability (JFP). The correlation index and Akaike Information Criterion are employed to optimize the Copula function. For illustration, a disconnector, representative switchgear equipment, was selected as an example and modeled with the experiment validation. Model analysis results demonstrate the Clayton type is the most competent for evaluation. Neglecting functional failure can significantly underestimate the actual failure probability at various seismic intensities, up to 50%. Furthermore, it was found that the current equipment material strength level already meets the requirements, and enhancing strength alone does not effectively improve reliability under strong earthquakes. For PGA intensities ≥0.7 g, the blade deformation capacity plays the primary reliability constraint, independent of material stress. A cost‐effective seismic design should optimize the combination of allowable material stress and blade deformation based on the JFP considering seismic intensity and targeted reliability.

Climate change impact on the compound flood risk in a coastal city

Compound flooding caused by a combination of rainfall and water level may occur in coastal cities. Under the influence of climate change, compound flooding is likely to be intensified. In this paper, using the main urban area of Haikou City as an example, we developed an event-based statistical-numerical hybrid framework to investigate the effects of sea level rise (SLR) and future rainfall changes on compound flood risk. Future rainfall was predicted by using five global climate models (GCMs) in CMIP5. A combination of GCMs and IPCC AR5 was used to predict the future SLR. The copula was then used to calculate the probabilistic risk of compound flooding (OR joint probability and failure probability) for historical and future scenarios, and the numerical model was utilized to evaluate the risk of inundation under historical and future scenarios. The probabilistic risk results show that both the probability and the failure probability of compound flooding will increase in the future. Output from GCMs indicate that when the OR event of the 5-year event (the compound flooding when at least one of the 5-year rainfall and the 5-year water level happens) occurs, the joint probability of future compound flooding will likely increase to 37.5%. The numerical model results indicate that the flood extent, flood depth and flood duration will increase under the influence of future rainfall and SLR. Under the impact of climate change, the flood depth in the FS5 case will increase by 22.85% and the flood extent in the FS5 case will increase by 41.42% compared to HS5. By controlling for variables, we also find that the combined impact of future changes in rainfall and water level on the magnitude of flooding is greater than the sum of their individual impacts. Finally, we find that water storage facilities and tide gates could be set up to mitigate compound flooding under climate change.

Deep neural network aided Monte Carlo simulation in solder joint failure probability analysis

In this paper, a novel method based on deep neural networks is proposed to consider the uncertainties of the influential parameters using Monte Carlo simulation. The method reduces the number of sampling by concentrating the sampling range into the state limit function of the power converter of interest. The proposed method is applied to a PV inverter and reduces the calculation time to 25% of the so-called method.

Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.

System Reliability Analysis for Truss Structures Based on Automatic Updated�Model

Multiple failure modes tend to be identified in the reliability analysis of a redundant truss structure. This identification process involves updating the model for identifying the next potential failure members. Herein we intend to update the finite element model automatically in the identification process of failure modes and further perform the system reliability analysis efficiently. This study presents a framework that is implemented through the joint simulation of MATLAB and APDL and consists of three parts: reliability index of a single member, identification of dominant failure modes, and system-level reliability analysis for system reliability analysis of truss structures. Firstly, RSM (response surface method) combines with a constrained optimization model to calculate the reliability indices of members. Then the β-unzipping method is adopted to identify the dominant failure modes, and the system function in MATLAB, as well as the EKILL command in APDL, is used to facilitate the automatic update of the finite element model and realize load-redistribution. Besides, the differential equivalence recursion algorithm is performed to approximate the reliability indices of failure modes efficiently and accurately. Eventually, the PNET (probabilistic network evaluation technique) is used to calculate the joint failure probability as well as the system reliability index. Two illustrative examples demonstrate the accuracy and efficiency of the proposed system reliability analysis framework through comparison with corresponding references.

An efficient and effective method for reliability assessment of project duration

ABSTRACT The reliability assessment of the project duration is essential for successful project management; this has prompted the development of various methods to assess whether a project will be completed within the target duration. In this study, an efficient and effective method for assessing the reliability of project duration based on the method of moments is proposed, which utilizes bivariate-dimension reduction technology and the third-moment reliability index. The proposed method can avoid the extensive calculations of various factors in the commonly used methods: the correlation coefficients between each pair of paths and the joint failure probability of each pair of representative paths. From two numerical examples, it can be concluded that the proposed method can obtain almost the same results as Monte Carlo simulations with less calculation.

Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors

The kinematic trajectory accuracy reliability is an essential index to evaluate the service performance of industrial robots. This study proposes a new kinematic trajectory accuracy reliability analysis method for industrial robots by integrating the sparse grid technique, the saddlepoint approximation method and copula functions. The novelty of this study lies in comprehending the intercorrelations among single point three-coordinate directional positioning errors and multiple points positioning errors on the kinematic trajectory. To start with, the statistical moments of the positioning errors at an arbitrary point on the trajectory of industrial robots are calculated by the extended sparse grid (SPGR) technique. The single-point positioning accuracy reliability is then evaluated by the saddlepoint approximation (SPA) method. The joint failure probability of positioning accuracy between any two points and the matrix of correlation coefficients of positioning errors among multiple points are subsequently calculated by copula functions. Two methods, namely the boundary theory and the multivariate normal distribution theory, are employed in the current study to calculate the failure probability of kinematic trajectory accuracy. Three examples are implemented to demonstrate the proficiency of the currently proposed methods.

A system reliability estimation method by the fourth moment saddle point approximation and copula functions

AbstractDespite many advances in the field of computational system reliability analysis, estimating the joint probability distribution of correlated non‐normal state variables on the basis of incomplete statistical data brings great challenges for engineers. To avoid multidimensional integration, system reliability estimation usually requires the calculation of marginal failure probability and joint failure probability. The current article proposed an integrated approach for estimating system reliability on the basis of the high moment method, saddle point approximation, and copulas. First, the statistic moment estimation based on the stochastic perturbation theory is presented. Thereafter, by constructing CGF (concise cumulant generating function) for the state variable with its first four statistical moments, a fourth moment saddle point approximation method is established for the component reliability estimation. Second, the copula theory is briefly introduced and extensively utilized two‐dimensional copulas are presented. The best fit copula for estimating the probability of system failure is selected according to the AIC (Akaike Information Criterion). Finally, the derived method is applied to three numerical examples for the sake of a comprehensive validation.

EFRADIR: An Enhanced FRAmework for DIsaster Resilience

This paper focuses on how to increase the availability of a backbone network with minimal cost. In particular, the new framework focuses on resilience against natural disasters and is an evolution of the FRADIR/FRADIR-II framework. It targets three different directions, namely: network planning, failure modeling, and survivable routing. The steady state network planning is tackled by upgrading a sub-network (a set of links termed the spine) to achieve the targeted availability threshold. A new two-stage approach is proposed: a heuristic algorithm combined with a mixed-integer linear problem to optimize the availability upgrade cost. To tackle the disaster-resilient network planning problem, a new integer linear program is presented for the optimal link intensity tolerance upgrades together with an efficient heuristic scheme to reduce the running time. Failure modeling is improved by considering more realistic disasters. In particular, we focus on earthquakes using the historical data of the epicenters and the moment magnitudes. The joint failure probabilities of the multi-link failures are estimated, and the set of shared risk link groups is defined. The survivable routing aims to improve the network’s connectivity during these shared risk link group failures. Here, a generalized dedicated protection algorithm is used to protect against all the listed failures. Finally, the experimental results demonstrate the benefits of the refined eFRADIR framework in the event of disasters by guaranteeing low disconnection probabilities even during large-scale natural disasters.

FATIGUE LIFE ASSESSMENT METHOD OF WELDED JOINTS IN RAILWAY GIRDERS

Purpose. To improve the fatigue life evaluating method of welded joints in railway girders based on experimental and theoretical studies of these structures operation. Methodology. The fatigue life assessment is based on the nonfailure operation probability, at which is greater than the operating time. The hypothesis of damages linear summation is used to determine the fatigue life and operating time of welded joints. Accumulated damage from the reference train is a measure of fatigue life and operating time. The amount of damage depends on the stresses and fatigue curve of a typical welded joint. Influence lines are used to determine the stresses in a construction member under moving load. The operating time of the construction member is calculated by cargo traffic mathematical modeling for the construction service life. Results. The method for assessing fatigue life is described. This method is taking into account the design features of the railway girder and the parameters of its operation. Originality. The numerical simulation of railway girder has allowed to establish the dependence of stresses on the beam web surface near the weld toe on the rigidity of stiffener fixation to beam flanges, to formulate rules for constructing bending moment influence lines, and to obtain a formula for determining stresses on the surface of the beam web under moving load. An equation is obtained for describing the fatigue curves of typical welded joints depending on the probability of joint failure. Practical value. A stochastic model for fatigue life predicting of typical railway welded girders is proposed. This model is taking into account the design features of the span and the parameters of its operation.

Multivariate Assessment of Low-Flow Hazards via Copulas: The Case Study of the Çoruh Basin (Turkey)

Bivariate modeling and hazard assessment of low flows are performed exploiting copulas. 7-day low flows observed, respectively, in the upper, middle and lower parts of the Çoruh basin (Turkey) are examined, considering three pairs of certified stations located in different sub-basins. A thorough statistical analysis indicates that the GEV distribution can be used to model the marginal behavior of the low-flow. The joint distributions at each part are modeled via a dozen of copula families. As a result, the Husler–Reiss copula adequately fits the joint low flows in the upper part, while the t-Student copula turns out to best fit the other parts. In order to assess the low-flow hazard, these copulas are then used to compute joint return periods and failure probabilities under a critical bivariate “AND” hazard scenario. The results indicate that the middle and lower parts of the Çoruh basin are likely to experience the largest drought hazards. As a novelty, the statistical tools used allow to objectively quantify drought threatening in a thorough multivariate perspective, which involves distributional analysis, frequency analysis (return periods) and hazard analysis (failure probabilities).

Microstructural Characterization and Unified Reliability Assessment of Aged Solder Joints in a PV Module

In this article, the finite-element method (FEM) along with experimental analysis was used to establish a mission profile-based reliability assessment of solder joints of the photovoltaic module under thermal cycling conditions. While considering the uncertainties of the solder joint lifetime model that leads to more realistic results, this method is able to estimate the time if the probability of solder joint failure reaches 10%. Several conditions, including standard and nonstandard cases, were simulated and performed to obtain the fatigue lifetime models. The experimental results, that is, scanning electron microscopy (SEM) along with X-ray tomography, showed that more intermetallic compounds and microvoids are formed in the nonstandard conditions (STCs). As a result, the non-STC leads to the accelerated failure in the solder interconnection, which is consistent with the FEM outcomes. In the end, a real case study was considered to investigate the efficiency of thermomechanical fatigue reliability model.

A factorial Bayesian copula framework for partitioning uncertainties in multivariate risk inference

In this study, a factorial Bayesian copula (FBC) method is proposed to quantify parameter uncertainties in copula-based models and then reveal their impacts on hydrologic risk inferences within a multivariate context. In detail, Bayesian inference and factorial analysis are integrated into copula-based multivariate risk models to (1) quantify parameter uncertainties, (ii) reveal their individual and interactive effects, and (iii) identify their detailed contributions to uncertain risk inferences. Streamflow observations at Xiangxi and Wei River basins is China are used to illustrate the applicability of FBC. The results indicate that imprecise parameters in marginal distributions and the dependence structure would lead to extensive uncertainties in predictive joint return periods and failure probabilities. Also, individual and interactive effects of parameters are well revealed through multilevel factorial analysis, and the detailed contributions of one parameter to different failure probabilities under different service time scenarios are identified.

Extracting unambiguous information from a single qubit by sequential observers

In a recent paper [Phys. Rev. Lett. 111, 100501 (2013)], a scheme was proposed where subsequent observers can extract unambiguous information about the initial state of a qubit, with finite joint probability of success. Here, we generalize the problem for arbitrary preparation probabilities (arbitrary priors). We discuss two different schemes: one where only the joint probability of success is maximized and another where, in addition, the joint probability of failure is also minimized. We also derive the mutual information for these schemes and show that there are some parameter regions for the scheme without minimizing the joint failure probability where, even though the joint success probability is maximum, no information is actually transmitted by Alice.

Oparta na niezawodności systemu optymalizacja konstrukcji łańcucha przenośnikowego zgrzebłowego o zależnych przyczynach uszkodzeń

With the incomplete probability information, the joint probability distribution modelling and system reliability-based optimization design of structural systems with failure interactions are challenging problems in the domain of reliability. This article is designed to propose a system reliability-based optimization design method for optimizing the scraper chain with multiple failure modes. Firstly, the common failure modes of the scraper chain are analysed. For each failure mode, a reliability model for the failure of scraper chains is obtained. Secondly, aiming at the joint failure probability modelling problem, a method for estimating the failure probability of the scraper chain based on system reliability is proposed. The reliability of scraper chains is calculated by the stochastic perturbation technique and the four-moment method. And then, the optimization design problem is discussed based on system reliability. And the optimal model is established. Finally, the effectiveness of the method is verified by the illustrative example of scraper chains. The proposed joint failure probability estimation method and design optimization are shown in the example. The results obtained can provide a reference for the optimal design of the scraper chain.

Compound effects of rainfall and storm tides on coastal flooding risk

Rainfall and storm tides are both flood drivers in coastal zones. The complex interplay between them can lead to or exacerbate the impacts of flooding. While the dependence between rainfall and storm tides has been extensively studied, the compound effects of them on coastal flood risk have not been well researched. With Haikou city as the case study, this study investigates the bivariate return period of compounding rainfall and storm tide events based on copula functions and the failure probability is used to assess the variation of bivariate flood risk during the entire project lifetime. The results show that (1) there is a significant correlation between rainfall and storm tides. Therefore, bivariate RP analysis can provide more adequate and comprehensive information about risks than univariate RP analysis. Kendall RP can describe the bivariate RP more accurately since the dangerous region of Kendall RP is divided by the joint probability value. (2) Neglecting the compounding impacts and the dependence of rainfall and storm tides causes significant underestimation of the joint RP and failure probability. (3) The bivariate hydrologic risk value will decrease quickly when the design rainfall is higher than 100 mm and it becomes small and decrease slowly when the design rainfall exceeds 450 mm. Furthermore, the bivariate hydrologic risk value would not decrease until the storm tide is higher than 2 m and the values would become quite small as the storm tide exceeds 4.5 m. Such bivariate hydrologic risk analysis can provide decision support for hydraulic facility design as well as actual flood control and mitigation.

A hybrid approach for reliability-based robust design optimization of structural systems with dependent failure modes

ABSTRACTEstimating system reliability has been recognized as a critical step in reliability-based design optimization for structural systems. The estimation of system reliability entails multi-dimensional integration, which is very difficult and numerically expensive. The proposed hybrid method integrates the concepts of saddlepoint approximation, the dimension reduction method (DRM) and copulas. The moment-based saddlepoint approximation enhanced by the DRM is used to calculate the component reliability. The copula method is adopted to realize the joint failure probability modelling between two failure modes. Reliability-based sensitivity analysis for the reliability-based robust design optimization is carried out as well. The structural system optimization model is established, including the Euclid norm of the reliability sensitivity as an optimization objective and system reliability as a constraint. Examples demonstrate that the optimization results obtained by the proposed approach ensure the maximization of the robustness of the structural system.

Kinematic Design of Optimally Fault Tolerant Robots for Different Joint Failure Probabilities

A measure of fault tolerance for different joint failure probabilities is defined based on the properties of the singular values of the Jacobian after failures. Using this measure, methods to design optimally fault tolerant robots for an arbitrary set of joint failure probabilities and multiple cases of joint failure probabilities are introduced separately. Given an arbitrary set of joint failure probabilities, the optimal null space that optimizes the fault tolerant measure is derived, and the associated isotropic Jacobians are constructed. The kinematic parameters of the optimally fault tolerant robots are then generated from these Jacobians. One special case, i.e., how to construct the optimal Jacobian of spatial 7R robots for both positioning and orienting is further discussed. For multiple cases of joint failure probabilities, the optimal robot is designed through optimizing the sum of the fault tolerant measures for all the possible joint failure probabilities. This technique is illustrated on planar 3R robots, and it is shown that there exists a family of optimal robots.