New method of online testing and data processing for LED lamps

In order to achieve quick and accurate lifetime prediction of LED lighting products under the testing time of 2 000 h, a method of online testing of luminous flux is proposed under the condition of temperature stress.Exponential fitting of lumen maintenance, the Bayesian estimation of failure probability, the Weibull distribution of lifetime and the Arrhenius model of the decay rate are used in combination to acquire the distribution of failure probability over time at the ambient temperatures of 25 ℃. The lifetime test of the same lamps based on the Energy Star standard under the testing time of 6 000 h is also implemented to verify the effectiveness of the method. The errors of lifetimes acquired with the proposed method are 7%, 4%, 3% and 1% at the failure probabilities of 62. 3%, 10%, 5% and 1%,respectively.

A personalized trustworthy service selection method

A personalized trustworthy service selection method is proposed to fully express the features of trust, emphasize the importance of user preference and improve the trustworthiness of service selection. The trustworthiness of web service is defined as customized multi-dimensional trust metrics and the user preference is embodied in the weight of each trust metric. A service selection method combining AHP （analytic hierarchy process） and PROMETHEE （preference ranking organization method for enrichment evaluations） is proposed. AHP is used to determine the weights of trust metrics according to users＇ preferences. Hierarchy and pairwise comparison matrices are constructed. The weights of trust metrics are derived from the highest eigenvalue and eigenvector of the matrix. to obtain the final rank of candidate services. The preference functions are defined according to the inherent characteristics of the trust metrics and net outranking flows are calculated. Experimental results show that the proposed method can effectively express users＇ personalized preferences for trust metrics, and the trustworthiness of service ranking and selection is efficiently improved.

Total plant performance evaluation based on big data: Visualization analysis of TE process

The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

Methodology for estimating probability of dynamical system's failure for concrete gravity dam

Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower construction.Reliability analysis for the dynamical dam safety should be divided into two phases:failure mode identification and the calculation of the failure probability.Both of them are studied based on the mathematical statistics and structure reliability theory considering two kinds of uncertainty characters(earthquake variability and material randomness).Firstly,failure mode identification method is established based on the dynamical limit state system and verified through example of Koyna Dam so that the statistical law of progressive failure process in dam body are revealed; Secondly,for the calculation of the failure probability,mathematical model and formula are established according to the characteristics of gravity dam,which include three levels,that is element failure,path failure and system failure.A case study is presented to show the practical application of theoretical method and results of these methods.

Discrepancies in surface temperature between NCEP reanalysis data and station observations over China and their implications

Previous studies show that temporal irreversibility(TI),as an important indicator of the nonlinearity of time series,is almost uniformly overestimated in the daily air temperature anomaly series over China in NCEP reanalysis data,as compared with station observations.Apart from this highly overestimated TI in the NCEP reanalysis,some other important atmospheric metrics,such as predictability and extreme events,might also be overestimated since there are close relations between nonlinearity and predictability/extreme events.In this study,these issues are fully addressed,i.e.,intrinsic predictability,prediction skill,and the number of extreme events.The results show that intrinsic predictability,prediction skill,and the occurrence number of extreme events are also almost uniformly overestimated in the NCEP reanalysis daily minimum and maximum air temperature anomaly series over China.Furthermore,these overestimations of intrinsic predictability,prediction skill,and the number of extreme events are only weakly correlated with the overestimated TI,which indicates that the quality of the NCEP reanalysis should be carefully considered when conclusions on both predictability and extreme events are derived.

Reliable evaluation method of quality control for compressive strength of concrete

Concrete in reinforced concrete structure (RC) is generally under significant compressive stress load. To guarantee required quality and ductility, various tests have to be conducted to measure the concrete’s compressive strength based on ACI (American Concrete Institute) code. Investigations of recent devastating collapses of structures around the world showed that some of the collapses directly resulted from the poor quality of the concrete. The lesson learned from these tragedies is that guaranteeing high quality of concrete is one of the most important factors ensuring the safety of the reinforced concrete structure. In order to ensure high quality of concrete, a new method for analyzing and evaluating the concrete production process is called for. In this paper, the indices of fit and stable degree are proposed as basis to evaluate the fitness and stability of concrete’s compressive strength. These two indices are combined to define and evaluate the quality index of the compressive strength of concrete. Prin-ciples of statistics are used to derive the best estimators of these indices. Based on the outcome of the study, a concrete compres-sive strength quality control chart is proposed as a tool to help the evaluation process. Finally, a new evaluation procedure to assess the quality control capability of the individual concrete manufacturer is also proposed.

Reliability Analysis Based on a Direct Ship Hull Strength Assessment

A method of reliability analysis based on a direct strength calculation employing the von Mises stress failure criterion is presented here. The short term strain distributions of ship hull structural components are identified through the statistical analysis of the wave-induced strain history and the long term distributions by the weighted summation of the short term strain distributions. The wave-induced long term strain distribution is combined with the still water strain. The extreme strain distribution of the response strain is obtained by statistical analysis of the combined strains. The limit state function of the reliability analysis is based on the von Mises stress failure criterion, including the related uncertainties due to the quality of the material and model uncertainty. The reliability index is calculated using FORM and sensitivity analysis of each variable that has effects on the reliability is also discussed.

Reliability analysis of a composite laminate using estimation theory

Composite laminates are made up of composite single-plies sequence. The plies generally have the same fiber and resin and their difference in fiber orientation results in a difference in various laminates' strength. Tsai-Hill failure criterion as a limiting state function to analyze structural reliability of a composite laminate and estimation theory in order to estimate statistical parameters of effective stress were utilized to construct probability box. Finally, we used the Monte Carlo simulation and FERUM software to calculate the upper and lower bounds of probability of failure.

Reduced Model for Power System State Estimation Using Artificial Neural Networks

In this paper, a new technique using artificial neural networks for power system state estimation is presented. This method does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on six-bus, 18-bus, IEEE 14-bus and IEEE 57-bus power systems and the results show that this method is very accurate and a lot faster than conventional techniques making it ideal for smart grid applications.

Structural reliability analysis and reliability-based design optimization: Recent advances

We review recent research activities on structural reliability analysis,reliability-based design optimization(RBDO) and applications in complex engineering structural design.Several novel uncertainty propagation methods and reliability models,which are the basis of the reliability assessment,are given.In addition,recent developments on reliability evaluation and sensitivity analysis are highlighted as well as implementation strategies for RBDO.

Reliability assessment of networks-on-chip based on analytical models

As technology scales down, the reliability issues are becoming more crucial, especially for networks-on-chip (NoCs) that provide the communication requirements of multi-processor systems-on-chip. Reliability evaluation based on analytical models is a precise method for dependability analysis before and after designing the fault-tolerant systems. In this paper, we accurately formulate the inherent reliability and vulnerability of some popular NoC architectures against permanent faults, also depending on the employed routing algorithm and traffic model. Based on this analysis, effects of failures in the links, switches and network interfaces on the packet delivery of NoCs are determined. Besides, some extensions to evaluate a fault-tolerant method and some routing algorithms are described. The analyses are validated through appropriate simulations. The results thus obtained are exactly the same as or very close to the analytical ones.