Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 6 Articles
Software reliability growth models (SRGMs) based on a nonhomogeneous Poisson process (NHPP) are widely used to describe\nthe stochastic failure behavior and assess the reliability of software systems. For these models, the testing-effort effect and the\nfault interdependency play significant roles. Considering a power-law function of testing effort and the interdependency of\nmultigeneration faults, we propose amodified SRGMto reconsider the reliability of open source software (OSS) systems and then to\nvalidate the model�s performance using several real-world data. Our empirical experiments show that the model well fits the failure\ndata and presents a high-level prediction capability. We also formally examine the optimal policy of software release, considering\nboth the testing cost and the reliability requirement. By conducting sensitivity analysis, we find that if the testing-effort effect or\nthe fault interdependency was ignored, the best time to release software would be seriously delayed and more resources would be\nmisplaced in testing the software....
The estimation of the cross-correlation of shear strength parameters (i.e., cohesion and internal friction angle) and the subsequent\ndetermination of the probability of failure have long been challenges in slope reliability analysis. Here, a copula-based approach\nis proposed to calculate the probability of failure by integrating the copula-based joint probability density function (PDF) on the\nslope failure domain delimited with the...
This study was an attempt to develop and measure the validity of a statistical module for educational research which can be\nan input to sustainable quality research among student-researchers of Laguna State Polytechnic University. In this study, it was\nhypothesized that evaluation on the developed module is significantly correlated to their performance. It was also hypothesized\nthe existence of significant difference between the extents of evaluation of groups of respondents on the characteristics of the developed\nmodule. Based on the result, the developed module in Statistics has very high extent of validity in terms of specific objectives, content,\nlanguage used, and evaluation activities. The utilization of the developed module leads students to have a very satisfactory performance.\nThis performance shows that they do not only know the concepts included in the subject but also they can apply statistics in real life\nsituations. Likewise, the performance of students has increased after the utilization of the developed module which is attributed to the\nattainment of the specific objectives in every lesson provided in the module and the language being used. In addition, if students were\ngiven step by step procedure which is easily understood, their performance would be increased....
Parametric Accelerated Life Testing (ALT) was used to improve the reliability of icemaker\nsystem with a fractured helix upper dispenser in field. By using bond graphs\nand state equations, a variety of mechanical loads in the assembly were analyzed. The\nacceleration factor was derived from a generalized life-stress failure model with a\nnew load concept. To reproduce the failure modes and mechanisms causing the\nfracture, new sample size equation was derived. The sample size equation with the\nacceleration factor also enabled the parametric accelerated life testing to quickly reproduce\nearly failure in field. Consequently, the failure modes and mechanisms\nfound were identical with those of the failed sample. The design of this testing should\nhelp an engineer uncover the design parameters affecting the reliability of fractured\nhelix upper dispenser in field. By eliminating the design flaws, gaps and weldline, the\nB1 life of the redesign of helix upper dispenser is now guaranteed to be over 10 years\nwith a yearly failure rate of 0.1% that is the reliability quantitative test specifications\n(RQ)....
In practical engineering, reliability analysis of products plays an important role in assessing the corresponding remaining\nlife. Products often have more than one failure mechanism, which means reliability should be evaluated by two or more\nperformance characteristics. To resolve the above problem, based on the degradation values distribution method utilization,\ncopula functions as the link functions of their marginal distributions are introduced in this study. The performance\ncharacteristics are also considered the dependent as well as independent to make a comparison. Moreover, a framework\nfor reliability assessment of products with the multiple performance degradation is developed. Finally, the proposed\nmethodology and model is validated effectively by the fatigue cracks data from the literature. From the results, we can\nsee that the proposed model is more accurate....
Selecting and using an appropriate structural reliability method is critical for the success of structural reliability analysis\nand reliability-based design optimization. However, most of existing structural reliability methods are developed and\ndesigned for a single limit state function and few methods can be used to simultaneously handle multiple limit state functions\nin a structural system when the failure probability of each limit state function is of interest, for example, in a\nreliability-based design optimization loop. This article presents a new method for structural reliability analysis with multiple\nlimit state functions using support vector machine technique. A sole support vector machine surrogate model for all\nlimit state functions is constructed by a multi-input multi-output support vector machine algorithm. Furthermore, this\nmulti-input multi-output support vector machine surrogate model for all limit state functions is only trained from one\ndata set with one calculation process, instead of constructing a series of standard support vector machine models which\nhas one output only. Combining the multi-input multi-output support vector machine surrogate model with direct\nMonte Carlo simulation, the failure probability of the structural system as well as the failure probability of each limit state\nfunction corresponding to a failure mode in the structural system can be estimated. Two examples are used to demonstrate\nthe accuracy and efficiency of the presented method....
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