Failure Probability Model Research Articles

Construction of adaptive Kriging metamodel for failure probability estimation considering the uncertainties of distribution parameters

A critical problem in engineering reliability analysis is obtaining an accurate failure probability with a high computational efficiency. This study aims to present failure probability estimation under the conditions of uncertain input variables and their uncertain distribution parameters. An adaptive Kriging model of failure probability with respect to distribution parameters (FP-DP model) is developed, which avoids coupling modeling among the distribution parameters, input variables, and failure probability. An improved U-learning function that simultaneously considers the statistical information of uncertain distribution parameters and failure probability is proposed to select new sampling points for the FP-DP model. The stopping criteria based on sample distances and relative errors of the predicted failure probability are constructed to improve the convergence performance around the limit state function. Three numerical and four engineering examples with different complexities are considered to verify the effectiveness of the proposed adaptive FP-DP Kriging metamodel. The results show that the proposed method can obtain an accurate failure probability with fewer sampling points of uncertain distribution parameters than some existing methods, indicating that the proposed method can be efficiently integrated into reliability-based design optimization problems considering both the uncertainties of input variables and their distribution parameters.

Slippage Failure Probability Model Using a Measurement Method for Tack Coat Residual Application Rates in Field Core

Abstract This study presents the development of a probability model for pavement slippage failure. A measurement method for a tack coat residual application rate in a field core through binder extraction was developed and verified. It was found that implementing a measurement method for tack coat quality acceptance is feasible. Pavement investigations were performed on two slippage failure cases in Indiana and confirmed that the residual application rates of slippage failed sections were significantly lower than those of non-failed sections. A slippage failure probability model in terms of residual application rates was developed through binary logistic regression analysis. The model can be used to determine appropriate residual application rates in consideration of its uniformity from a distributor and to develop tack coat specifications based on slippage failure probability.

Reliability Assessment Model and Simulation of Journal Bearing of Railway Freight Cars Based on Bayesian Method under Small Sample Sizes

Generally, the box bearing of railway freight cars has no bearing sample failure data at the end of the time-terminated reliability test. However, it is expensive and has high service reliability requirements. Given a small sample size and zero-failure data, the traditional failure probability calculation formula based on a large sample size and the reliability modeling technique cannot easily assess the reliability of rolling bearings accurately. Considering the applicability of the bearing of railway freight cars, this study integrated the prior information of samples and the simulation test information according to Bayes statistical theory, deduced the mathematical model of cumulative failure probability under failure-free data, calculated the distribution parameters using the least square method, and established the reliability estimation model of rolling bearings on the basis of Weibull distribution. The failure-free simulation data of rolling bearings were produced according to the Monte Carlo simulation, and the reliability of the journal bearing of railway freight cars was simulated and assessed by three methods. Simulation results demonstrate that the proposed reliable Bayes multilayer estimation method could not only meet the design requirements of the ISO 281 rolling bearing standards on that basis of the failure-free data and small sample size of the time-terminated simulation, but also assess the reliability of the rolling bearing of railway freight cars.

Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas

Currently, quantitative risk assessment is often used for pipeline safety analysis. However, the low probability of pipeline failure may produce a false safety evaluation result because high-consequence areas with high population densities cannot be effectively identified. Furthermore, nearly half of the gas leakage accidents in China have occurred in densely populated areas. Therefore, an improved quantitative risk assessment method is proposed. First, we establish two models: (1) a failure probability model based on improved historical failure and disaster derivation probabilities and (2) a risk consequence model considering potential direct and indirect losses based on the probability of disaster evolution. Considering the concept of human rights equality and the social model of “life is first” in China, a method to correct the loss value of life according to population density is proposed, which can effectively avoid the hidden phenomenon of high-consequence areas mentioned above. A pipeline in China is evaluated using this improved method. Compared with traditional evaluation results, the new evaluation method can effectively identify a high-consequence area and obtain more reasonable results. Thus, a pipeline maintenance plan can ensure the interests of enterprises and fully respect the lives of individuals threatened by the potential risk of the pipeline.

Failure probability model and meso-degradation law of high performance cement-based composites in salt-frozen environment

In order to investigate the damage characteristics and degradation law of high performance cement-based composites (HPCC) in salt-frozen environment, the effect of freeze-thaw cycles on the loss rate of relative dynamic modulus was analyzed, based on the theories of damage mechanics, fracture mechanics and fractal geometry, deriving an equation to determine the failure probability of HPCC in salt-frozen environment.The equation not only considers the effect of cycle number on freeze-thaw damage, but also introduces the fractal dimension of bubble distribution.The freeze-thaw cycles of HPCC in different test groups were carried out to verify the model, and the pore structure parameters of HPCC in different freeze-thaw cycles were tested to analyze its meso-degradation law, the models of bubble distance coefficient and average chord length are established, which can quantitatively characterize HPCC meso-scale degradation characteristics.The results showed that:the calculated results of the salt freezing failure probability model were in good agreement with the test results. The model can accurately describe the failure probability of HPCC with different freeze-thaw cycles.The meso-degradation rule indicates that with the increase of freeze-thaw cycles, the small holes and micro-cracks in the matrix transition to the big holes, the matrix structure changes from compact and hard to porous, and the frost resistance durability decreases significantly.The meso-structure degradation model can quantitatively calculate the characteristic values of bubble spacing coefficient and average chord length degradation of bubbles, and provide a non-destructive evaluation method for subsequent HPCC meso-structure degradation research. Highlights Deriving an equation to determine the failure probability of HPCC in salt-frozen environment. The meso-degradation rule of HPCC was found that the transition from small holes and micro-cracks to big holes in the matrix. A relationship model for quantitative characterization of the eigenvalues of meso-structure degradation was established.

Safety enhancement design method and control strategy for CCU of high-speed train

The Train Control and Monitoring System (TCMS) is the communication command center of the train, and the Central Control Unit (CCU) is its core component, which should achieve SIL4 safety integrity level defined in standard EN50129. Comparing the current CCU safety mode of dual-machine hot standby to the double 2 out of 2 (2oo2) structure, this paper find that the latter performances better in security. So a new CCU architecture with enhanced safety is proposed, the double 2oo2 structure is applied to the CCU design, and the Markov failure probability model is established to analyze its safety quantitatively. The CCU based on double 2oo2 redundant structure could meet the failure safety principle of composite fail-safety and reactive fail-safety stipulated in the SIL4 safety integrity level, and its Tolerable Functional Failure Rate (TFFR) could reach the requirements of the SIL4. As another important part of RAMS (Reliability, Availability, Maintainability, and Safety), the reliability of this CCU architecture is evaluated both qualitatively and quantitively. According to the results, the double 2oo2 structure could greatly improve the safety of the TCMS and can be used in railway signal system with SIL4 safety integrity requirements.

Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

With the development of communication technology, train control operation system develops gradually, which significantly improves the reliability and efficiency of train operation. The current mobile Internet has gradually highlighted the many limitations of the mobile Internet in the high-speed mobile environment, which seriously deteriorate the service quality and user experience, and cause a waste of resources. In order to meet the real-time requirements of network communication resource scheduling in the mobile environment, aiming at the multidimensional dynamic adaptation framework constructed in a mobile environment, a service and network adaptation mechanism based on link failure state prediction is proposed in the paper. First, cross-layer theoretical analysis and actual data analysis are combined to construct a wireless link failure probability model. Then, reliable transmission requirements and transmission overhead are applied to optimize goals. Finally, simulation experiments are carried out according to the railway network data to evaluate the E-GCF adaptation algorithm. The experiment results show that compared with the current mainstream algorithms, the prediction accuracy of this adaptation algorithm is improved by 25%. The execution time of the algorithm is reduced by 9.6 seconds and the successful submission rate is as high as 99.99%. The advantages of the algorithm are significantly superior other algorithms. It proves that the research method of this paper can effectively improve the satisfaction rate and utility value of reliable transmission, as well as enhance the throughput performance. It solves the adaptation problems of frequent switching and low utilization of heterogeneous networks in a mobile environment, which contributes to the high-quality communication service of mobile network.

Distributed collaboration and anti-interference optimization in edge computing for IoT

The edge computing (EC) systems for Internet of Things (IoT) can bring out low latency, high reliability, distributed intelligence, and network bandwidth savings to industrial real-time applications. However, limited computing and processing capabilities of edge devices remains to be difficult to meet complex data processing and artificial intelligence (AI) analysis requirements for diverse services. Besides, the scarcity of wireless spectrum resources in harsh industrial environment makes the interference between devices more serious. To address these challenges, this paper proposes an adaptive distributed collaborative anti-interference optimization scheme for IoT-edge system. Firstly, the EC system model is established, and the model of link failure probability is derived theoretically. Then, an Occupy-Interference Mitigation (O-IM) algorithm based on full-frequency multiplexing is proposed. The algorithm combines adaptive full-frequency multiplexing and interference mitigation to reduce the dependence of the reliable collaboration on bandwidth and signal-to-noise ratio (SNR). In addition, an anti-interference algorithm based on Interference Mitigation and inter-cellfrequency multiplexing Interference Avoidance (IM-IA) is proposed to balance bandwidth and SNR. This algorithm adopts interference cancellation scheme in the broadcasting phase, and adopts orthogonal frequency division in the collaboration phase. Extensive simulation results on Mininet platform verify that the proposals can obtain lower failure probability, and are less than traditional solutions in transmitting power, bandwidth, and SNR requirements. Moreover, the O-IM is suitable for low power scenarios, while the IM-IA is more suitable for high power case.

Research on optimal maintenance cycle model of nuclear equipment based on left and right fuzzy sorting method

For the nuclear equipment with high reliability, the optimal maintenance cycle model of nuclear equipments is established by balancing the relationship between working time before component failure and maintenance cost. According to the expert evaluation, the failure of the equipment is analyzed. In the study, the ridge membership function is improved, and the failure probability model is established by using the left and right fuzzy sorting method. It transforms the fuzzy number into the fuzzy probability, so the component fault distribution function is determined. By testing, the model has good applicability and sensitivity. Taking the spent fuel shearing machine as an example, the optimal maintenance cycle of the key parts of the shearing machine is calculated. The results are consistent with the actual conditions, which can provide the basis for the formulation of the nuclear equipment maintenance plan. The results of the study show that the proposed method is of great significance for improving the reliability and maintenance efficiency of nuclear equipment.

Effect of Weibull parameters and crack distribution on the failure probability of multi-layered SiC cladding

In this paper, an improved Weibull failure probability model which considers initial cracks distributed in the cladding has been proposed. Considering the coupling of force, heat and irradiation acting on the multi-layered SiC cladding, failure probabilities of the cladding under different conditions have been obtained. The results indicate that compared to linear heat rate, the cladding is more sensitive to changes in inner pressure of fuel elements. The cladding can be optimized by adjusting the proportion of different layers. Further investigations indicate that effect of different Weibull parameters of CVD SiC on the failure probability is small and can be ignored in real fuel elements. Meanwhile, cracks in SiC composite layer and crack length have little effect on the failure probability. However, the initial inclination angle can affect the failure probability greatly. Results indicate that the most dangerous inclination angle in the cladding is about 73°.

Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

Fracture performance of GFRP-RC beams with working cracks in alkaline environment for eight years

The development trend of glass fiber-reinforced polymer-reinforced concrete (GFRP-RC) beams partially or entirely replacing reinforced concrete beams has gained acceptance. However, related research on long-term fracture durability is insufficient. This paper aims to investigate the long-term fracture durability of GFRP-RC beams, 36 GFRP-RC beams under the alkaline corrosion environment and sustained load for eight years were subjected to three-point eccentric loading experiments. The load crack opening distance (P-CMOD) curve and the corresponding fracture energy were obtained. The results show that the bearing capacity and residual fracture energy retention (ER) of GFRP-RC beams with working cracks in the APS (alkaline + pre-cracks + sustained load) group were 18.71% and 26.38% lower, respectively than those in the AS (alkaline + sustained load) group. After analyzing the critical CMOD curves for each service period with failure probabilities of 60% and 85%, a long-term crack growth failure probability model for the service time t and the critical CMOD was proposed, which provide a non-destructive damage assessment method for GFRP-RC beams in actual service.

Identification of Critical Links in Urban Road Network considering Cascading Failures

The identification of critical road links is greatly important to the management and control of the transportation system. Existing works fail to fully consider the influence of the distribution of traffic flow and its dynamic characteristics on critical road link identification. In this paper, we propose a criticality calculation method for urban road networks considering the effect of cascading failures which models the distribution change of traffic flow after a specific road link failed. Firstly, a sequence diagram calculation method is proposed to model how the traffic failure on one road link propagates to related links. Secondly, the diagram of the cascade failure sequence is divided into different stages according to the consistency of the objective function. The influence value of each stage is computed for the target road link. Finally, the failure probability model and the importance indicator are proposed to calculate the criticality for each road link. We evaluate our critical road link identification method on both simulated and read scenes. In our simulation, we achieve 90.6% and 91.7% for the accuracy on two key metrics, respectively, i.e., the length of failure road link and the total parking delay, which proves the feasibility of our method. Our method also achieves reasonable conclusions on real data and helps to find the critical road links.

Investigation on the fracture and mechanical behaviors of simulated transversely isotropic rock made of two interbedded materials

Transversely isotropic rock composed of two interbedded layers has been rarely studied, although landslides frequently occur in bedded rock masses. With reference to the interbedded rock mass in the Miaowei Reservoir area of Yunnan Province, China, this study prepares transversely isotropic specimens made of two interbedded materials to investigate the fracture and mechanical behaviors of interbedded rock. The effect of the bedding plane dip angle and the application of the findings to an anti-dip interbedded rock slope are also studied. Specifically, two groups of isotropic specimens and five groups of transversely isotropic specimens are tested in a series of triaxial compressive tests; based on these tests, the crack types, failure modes and mechanical parameters are analyzed, and one damage model and its application to the failure probability analysis of an anti-dip interbedded rock slope are investigated. The experimental results show that twelve crack types, eight failure modes and five axial stress-strain curve types can be summarized and are significantly affected by the dip angle. The recognition of these crack types and failure modes can improve the qualitative instability analysis of bedded rock masses in the reservoir area, and five mechanical parameters, including peak stress, strain related to peak strength, residual strength, elastic modulus and Poisson's ratio, exhibit fluctuating variation trends versus the dip angle and confining stress due to the influence of the dip angle. Two groups of theoretical curves from the proposed damage model correspond with the pre-peak features of the test stress-strain curves. The proposed failure probability model considering rock damage shows good practicality when applied to an anti-dip interbedded rock slope; the slope displays discrete failure probabilities in various parts, e.g., the slope toe, slope shoulder and weaker layer show higher failure probabilities, and the relaxation behavior increases the failure probabilities and gradually eliminates the discreteness. Relevancy analysis shows that the strength of transversely isotropic rock is more sensitive to the elastic modulus, dip angle of bedding plane, confining stress and composition of the rock.

An efficient reliability analysis strategy for low failure probability problems

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.

Reliability analysis of software-defined satellite network based on control delay

At present, there are few studies about the link failure and reliability of software defined satellite network (SDSN). However, the inter-satellite links (ISLs) may break down because of the long distance between the satellites, the high relative moving speed and the easy interference of the wireless channel. At the same time, most researches on controller deployment in SDSN only consider the transmission delay but ignore the processing delay. To solve these problems, considering the failure probability of intersatellite link, the failure probability model of ISLs is designed, the queuing delay and processing delay are added into the delay model, and the software defined satellite network reliability analysis model based on control delay is established. This model can describe the reliability of the network against link failure. The simulation results show that the model is consistent with the actual situation and can provide some reference value for controller deployment in SDSN.

Prediction of the failure probability of the overhead power line exposed to large-scale jet fires induced by high-pressure gas leakage

A thermal failure model (TFM) is proposed to predict the failure probability of Aluminum Conductor Steel-Reinforced (ACSR) typed power line close to a large-scale jet fire of leaked high-pressure gases. It introduces a newly developed method for heat transfer from jet fires and a distribution model for conductor failure probability via IEEE Standard 738–2012. Comparisons covering van der Waals equation, jet flame length correlations (Chamberlain, Schefer, Molkov and Bradley) and thermal radiation models (point source, multi-point source and line source) were made to illustrate priority with respect to experimental measurement of large hydrogen and natural gas jet fires. Results show that a theoretical framework incorporating van der Waals equation, Molkov's correlation for jet flame length, radiative fraction model and point source model is adequately precise to predict high-pressure leakage process, total flame length and received radiant heat flux (far-field). Predicted total flame lengths of a large jet fire for nearby power lines within 50–200 m to the accident site correspond well to reported results and the conservative hazard ranges are predicted based on harm criteria of wood and Probit equations. In simulations, an acceptable safety distance for power line carrying 907 A and below is determined to be 150 m.

Prognostic profiling of children with serious post-operative complications: A novel probability model for failure to rescue

BackgroundFailure to rescue (FTR), mortality after a major postoperative complication, is a superior surgical quality metric compared to surgical mortality or complications rates alone. Our objective was to develop and validate a novel pediatric profiling to identify high-risk subjects among the subset of children who develop serious post-operative complications. MethodsWe performed a retrospective study of children who developed one or more serious postoperative complications following inpatient surgery across NSQIP-Pediatric hospitals (2012–2017). We evaluated the rate of FTR according to pre-operative comorbidity burden. ResultsWe identified 45,504 surgical cases with major post-operative complications (FTR rates: 2.4%). Surgical cases with greater than six pre-operative comorbidities (n = 12,148;28%) accounted for 80% of FTR events. The expected probability of FTR was 0.1%(95%CI:0.1%–0.2%) among low-risk cases, 3.3%(95%CI:3.0%–3.5%) among intermediate-risk cases, and 22.6%(95%CI:20.9%–24.3%) among high-risk cases. About half of surgical cases in the high-risk profile group died within 48 h of surgery. Comparatively, cases in the intermediate-risk group had a much longer time to mortality (10 days). ConclusionWe propose a prognostic index to accurately identify children at risk for FTR. The use of such an index may provide surgeons with a window of opportunity to implement aggressive monitoring and therapeutic strategies to reduce mortality. Level of evidenceIV

Commentary regarding prognostic profiling of children with serious postoperative complications: A novel probability model for failure to rescue

This is a commentary on the manuscript by Mpody C, Arends J, Aldrink J, et al., titled “Prognostic Profiling of Children with Serious Post-Operative Complications: A Novel Probability Model for Failure to Rescue”.

Bayesian model updating for the corrosion fatigue crack growth rate of Ni-base alloy X-750

Nickel base Alloy X-750, which is used as fastener parts in light-water reactor (LWR), has experienced many failures by environmentally assisted cracking (EAC). In order to improve the reliability of passive components for nuclear power plants (NPP's), it is necessary to study the failure mechanism and to predict crack growth behavior by developing a probabilistic failure model.In this study, The Bayesian inference was employed to reduce the uncertainties contained in EAC modeling parameters that have been established from experiments with Alloy X-750. Corrosion fatigue crack growth rate model (FCGR) was developed by fitting into Paris’ Law of measured data from the several fatigue tests conducted either in constant load or constant ΔK mode.These parameters characterizing the corrosion fatigue crack growth behavior of X-750 were successfully updated to reduce the uncertainty in the model by using the Bayesian inference method. It is demonstrated that probabilistic failure models for passive components can be developed by updating a laboratory model with field-inspection data, when crack growth rates (CGRs) are low and multiple inspections can be made prior to the component failure.