Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder.Firstly,multivariate Bayesian dynamic linear model(MBDLM)considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections;secondly,with the proposed MBDLM,the dynamic correlation coefficients between any two performance functions can be predicted;finally,based on MBDLM and Gaussian copula technique,a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder,and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research.This can be done by having less complex applications.While developers and other experts have made signicant efforts in this context,the level of reliability is not the same as it should be.Therefore,further research into the most detailed mechanisms for evaluating and increasing software reliability is essential.A signicant aspect of growing the degree of reliable applications is the quantitative assessment of reliability.There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software.However,none of these mechanisms are useful for all kinds of failure datasets and applications.Hence nding the most optimal model for reliability prediction is an important concern.This paper suggests a novel method to substantially pick the best model of reliability prediction.This method is the combination of analytic hierarchy method(AHP),hesitant fuzzy(HF)sets and technique for order of preference by similarity to ideal solution(TOPSIS).In addition,using the different iterations of the process,procedural sensitivity was also performed to validate the ndings.The ndings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.

Reliability Prediction Method Based on State Space Model for Rolling Element Bearing

Reliability analysis based on equipment's performance degradation characteristics is one of the significant research areas in reliability research. Nowadays, many researches are carried on multi-sample analysis. But it is limited for a single equipment reliability prediction. Therefore, the method of reliability prediction based on state space model(SSM) is proposed in this research. Feature energy of the monitored signals is extracted with the wavelet packet analysis and the associated frequency band energy with online monitored data. Then,degradation feature is improved by moving average filtering processing taken as input pair model parameter of SSM to be estimated. In the end, state space predicting model of degradation index is established. The probability density distribution of the degradation index is predicted, and the degree of reliability is calculated. A real testing example of bearing is used to demonstrate the rationality and effectiveness of this method. It is a useful method for single sample reliability prediction.

Reliability prediction and its validation for nuclear power units in service

In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the main- tenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent avail- ability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council （NERC） and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of-2.16% to 5.23% and -2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and mainte- nance optimization methods for nuclear power units.

Predicting Reliability of Tactical Network Using RBFNN

A description of the reliability evaluation of tactical network is given, which reflects not only the non-reliable factors of nodes and links but also the factors of network topological structure. On the basis of this description, a reliability prediction model and its algorithms are put forward based on the radial basis function neural network (RBFNN) for the tactical network. This model can carry out the non-linear mapping relationship between the network topological structure, the nodes reliabilities, the links reliabilities and the reliability of network. The results of simulation prove the effectiveness of this method in the reliability and the connectivity prediction for tactical network.

RESEARCH PROGRESS ON STRUCTURAL FATIGUE RELIABILITY DESIGN AND ANALYSIS METHODS OF CHINESE RAILWAY VEHICLES

A state-of-art review is given to the new advances on fatigue reliability design and analysis methods of Chinese railway vehicle＇s structures. First, the structures are subject to a complicated random fatigue stressing history and this history should be determined by combining dynamic simulation and on-line inspection. Second, the random fatigue constitutions belong to an intrinsic fatigue phenomenon and a probabilistic model is developed to well describe them with two measurements of survival probability and confidence, similar model is also presented for the random stress-life rela- tions and extrapolated appropriately into Song fatigue life regime. Third, concept of the fatigue limit should be understood as the fatigue strength at a given fatigue life and a so-called local Basquin model method is proposed for measuring the random strengths. In addition, drawing and application methods of the Goodman-Smith diagram for integrally characterizing the random fatigue strengths are established in terms of ten kilometers. Fourth, a reliability stress-based method is constructed with a consideration of the random constitutive relations. These new advances form a new frame work for railway fatigue reliability design and analysis.

Determination of Time for Conducting Preventive Maintenance Using Hybrid Analysis

It becomes a common practice to determi ne time for conducting preventive maintenance (PM) using a hazard function and an alarm limit on a specified failure rate. In this paper, the authors argue that u sing both hazard and reliability functions can improve the accuracy of the resul t, especially when the whole-life characteristic failure is modelled using diff erent failure distributions. The PM time predicted based on the hazard function should be checked against reliability.

Using PoF models to predict system reliability considering failure collaboration

Existing Physics-of-Failure-based （PoF-based） system reliability prediction methods are grounded on the independence assumption, which overlooks the dependency among the compo- nents. In this paper, a new type of dependency, referred to as failure collaboration, is introduced and considered in reliability predictions. A PoF-based model is developed to describe the failure behavior of systems subject to failure collaboration. Based on the developed model, the Bisection-based Reliability Analysis Method （BRAM） is exploited to calculate the system reliability. The developed methods are applied to predicting the reliability of a Hydraulic Servo Actuator （HSA）. The results demonstrate that the developed methods outperform the traditional PoF-based reliability prediction methods when applied to systems subject to failure collaboration.

An embeddable SOC real-time prediction technology for TDDB

This paper presents an embeddable SOC real-time prediction circuit and method for TDDB.When the SOC under test is fails due to TDDB,the prediction circuit is capable of issuing a warning signal.The prediction circuit,designed by using a standard CMOS process,occupies a small silicon area and does not share any signal with the circuits under test,therefore,the possibility of interference with the surrounding circuits is safely excluded.

Reliability Growth Test Planning and Verification of Commercial Vehicles

Reliability and durability are two important technical indicators in automobile research and development.A research-and-design and testing organization can increase inherent quality attributes by adopting a systematic approach based on statistical tools and clearly defined processes.The process affects the design phase,validation through testing,and quality assurance in production.On the basis of reliability growth theory and the Duane model,this study established an estimation method for the definition of the target mileage and specific test cycles in reliability growth testing.A construction method for defin-ing test conditions was proposed that adopts the theory of the design of experiments.The simulation was conducted under a variety of typical test conditions including differing operation times,loads,and logistics modes to predict customer use and detect failures.Failure cases were then analyzed in detail.At the same time,a reliability growth prediction model was established on the basis of the initial test data and used for test process tracking and risk control.

Fatman： Building Reliable Archival Storage Based on Low-Cost Volunteer Resources

We present Fatman, an enterprise-scale archival storage based on volunteer contribution resources from underutilized web servers, usually deployed on thousands of nodes with spare storage capacity. Fatman is specifically designed for enhancing the utilization of existing storage resources and cutting down the hardware purchase cost. Two major concerned issues of the system design are maximizing the resource utilization of volunteer nodes without violating service level objectives （SLOs） and minimizing the cost without reducing the availability of archival system. Fatman has been widely deployed on tens of thousands of server nodes across several datacenters, providing more than 100 PB storage capacity and serving dozens of internal mass-data applications. The system realizes an efficient storage quota consolidation by strong isolation and budget limitation, to maximally support resource contribution without any degradation on host-level SLOs. It novelly improves data reliability by applying disk failure prediction to minish failure recovery cost, named fault-aware data management, dramatically reduces the mean time to repair （MTTR） by 76.3% and decreases file crash ratio by 35% on real-life product workload.