Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
Evaluating the reliability ofMultistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or\nvariants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with\nlarge-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN OBDD and MFN MDD, are proposed\nin this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state\nspace of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced.\nMeanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1)\ncompared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The\nnumber of nodes and the number of variables of MDD generated in MFN MDD algorithm are much smaller than those of OBDD\nbuilt in the MFN OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for\ncalculating the reliability of sparse networks....
Bridge monitoring systems provide a huge number of stress data used for reliability prediction. In this article, the\ndynamic measure of structural stress over time is considered as a time series, and considering the limitation of the existing\nBayesian dynamic linear models only applied for short-term performance prediction, Bayesian dynamic nonlinear\nmodels are introduced. With the monitored stress data, the quadratic function is used to build the Bayesian dynamic\nnonlinear model. And two methods are proposed to handle with the built Bayesian dynamic nonlinear model and the\ncorresponding probability recursion processes. One method is to transform the built Bayesian dynamic nonlinear model\ninto Bayesian dynamic linear model with Taylor series expansion technique; then the corresponding probability recursion\nprocesses are completed based on the transformed Bayesian dynamic linear model. The other one is to directly handle\nwith the built Bayesian dynamic nonlinear model and the corresponding probability recursion processes with Markov\nchain Monte Carlo simulation method. Based on the predicted stress information (means and variances) of the above\ntwo methods, first-order second moment method is adopted to predict the structural reliability indices. Finally, an actual\nengineering is provided to illustrate the application and feasibility of the above two methods....
Accelerated degradation testing (ADT) has been widely used for reliability prediction of highly reliable products. In many\napplications, ADT data consists of multiple degradation-related features, and these features are usually dependent. When dealing\nwith such ADT data, it is important to fully utilize the multiple degradation features and take into account their inherent\ndependency. This paper proposes a novel reliability-assessment method that combines Brownian motion and copulas to model\nADT data obtained from vibration signals. In particular, degradation feature extraction is first carried out using the raw vibration\nsignals, and a feature selection method quantifying feature properties, such as trend ability,monotonicity, and robustness, is adopted\nto determine the most suitable degradation features. Then, a multivariate s-dependent ADT model is developed, where a Brownian\nmotion is used to depict the degradation path of each degradation feature and a copula function is employed to describe the\ndependence among these degradation features. Finally, the proposed ADT model is demonstrated using the vibration-based ADT\ndata for an electric motor....
Conventional reliability assessment and reliability-based optimal design of belt drive are based on the stressââ?¬â??strength\ninterference model. However, the stressââ?¬â??strength interference model is essentially a static model, and the sensitivity\nanalysis of belt drive reliability with respect to design parameters needs further investigations. In this article, timedependent\nfactors that contribute the dynamic characteristics of reliability are pointed out. Moreover, dynamic reliability\nmodels and failure rate models of belt drive systems under the failure mode of slipping are developed. Furthermore,\ndynamic sensitivity models of belt drive reliability based on the proposed dynamic reliability models are proposed. In\naddition, numerical examples are given to illustrate the proposed models and analyze the influences of design parameters\non dynamic characteristics of reliability, failure rate, and sensitivity functions. The results show that the statistical properties\nof design parameters have different influences on reliability and failure rate of belt drive in cases of different values of\ndesign parameters and different operational durations....
This paper proposes a method to evaluate the reliability of power system with different\ncapacities of wind power while considering carbon tax. The proposed method\nis a hybrid approach which combines Frequency and Duration (F&D) method\nand Monte Carlo Simulation (MCS) method. MCS method is used to achieve a model\nto simulate the random status of power system. Also, the proposed method is applied\non the IEEE 14-bus test system to investigate the effects of integrating different\ncapacities of wind energy to the reliability of power system with considering carbon\ntax....
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