Luis Alberto Rodríguez-Picón

Reliability Estimation for Products Subjected to Two-Stage Degradation Tests Based on a Gamma Convolution

Degradation models have received much attention in the area of reliability estimation, mostly because it is possible to obtain robust information about the lifetime of highly reliable products and systems. However, in the last years, multivariate models have received more attention. This is because the quality of many products is a function of various environmental conditions, and the effect of these conditions over a performance characteristic can cause a failure of the product, either marginally or jointly. A gamma process is considered in this study to marginally model the degradation of a performance characteristic through two degradation test phases performed sequentially. The degradation increments obtained during the first test and the second test are modeled jointly considering the convolution of two marginal gamma processes. In this way, it is possible to obtain a robust model to get reliability estimates considering the effect of two serial degradation test, which can consider multiple covariates. This modeling is illustrated with crack propagation data and important results are presented. Copyright

Reliability analysis for electronic devices using beta-Weibull distribution

Today, in reliability analysis, the most used distribution to describe the behavior of electronic products under voltage profiles is the Weibull distribution. Nevertheless, the Weibull distribution does not provide a good fit to lifetime datasets that exhibit bathtub-shaped or upside-down bathtub–shaped (unimodal) failure rates, which are often encountered in the reliability analysis of electronic devices. In this paper, a reliability model based on the beta-Weibull distribution and the inverse power law is proposed. This new model provides a better approach to model the performance and fit of the lifetimes of electronic devices. To estimate the parameters of the proposed model, a Bayesian analysis is used. A case study based on the lifetime of a surface mounted electrolytic capacitor is presented, the results showed that the estimation of the proposed model differs from the inverse power law–Weibull and that it affects directly the mean time to failure, the failure rate, the behavior, and the performance of the capacitor under analysis.

Modelling degradation with multiple accelerated processes

Abstract Multivariate degradation models are important tools for reliability assessment of highly reliable products. The multivariate aspect of the models depends on the device under study and the type of test applied to it. A device may exhibit different performance characteristics (PC) or one PC may be affected by multiple environmental conditions. If a device is affected by multiple environmental conditions, then it is possible to subject it to multiple accelerating variables in an accelerated degradation test in order to obtain reliability information. In this paper, two accelerating stress variables are considered, which characterize two degradation processes (DP) on a PC. In this way, two degradation models based on the life–stress Arrhenius relationship and the inverse power law relationship are provided. It is shown that the final distribution function characterizing the failure times for each DP can be used to estimate the reliability of a fuel sensor, the example under study in this paper. The interaction of the models with respect to the DPs is described via copula1 function, in order to model the dependency structure. A Bayesian approach is proposed to estimate the parameters of the final models and the best fit models are selected using information criteria.

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

The multiobjective optimization on the basis of ratio analysis (MOORA) method captures diverse features such as the criteria and alternatives of appraising a multiple criteria decision-making (MCDM) problem. At the same time, the multiple criteria problem includes a set of decision makers with diverse expertise and preferences. In fact, the literature lists numerous approaches to aid in this problematic task of choosing the best alternative. Nevertheless, in the MCDM field, there is a challenge regarding intangible information which is commonly involved in multiple criteria decision-making problem; hence, it is substantial in order to advance beyond the research related to this field. Thus, the objective of this paper is to present a fused method between multiobjective optimization on the basis of ratio analysis and Pythagorean fuzzy sets for the choice of an alternative. Besides, multiobjective optimization on the basis of ratio analysis is utilized to choose the best alternatives. Finally, two decision-making problems are applied to illustrate the feasibility and practicality of the proposed method.

Reliability Estimation for Accelerated Life Tests Based on a Cox Proportional Hazard Model with Error Effect

In this paper, a Cox proportional hazard model with error effect applied on the study of an accelerated life test is investigated. Statistical inference under Bayesian methods by using the Markov chain Monte Carlo techniques is performed in order to estimate the parameters involved in the model and predict reliability in an accelerated life testing. The proposed model is applied to the analysis of the knock sensor failure time data in which some observations in the data are censored. The failure times at a constant stress level are assumed to be from a Weibull distribution. The analysis of the failure time data from an accelerated life test is used for the posterior estimation of parameters and prediction of the reliability function as well as the comparisons with the classical results from the maximum likelihood estimation. Copyright

Bivariate degradation modelling with marginal heterogeneous stochastic processes

ABSTRACT In this paper, we consider that the degradation of two performance characteristics of a product can be modelled by stochastic processes and jointly by copula functions, but different stochastic processes govern the behaviour of each performance characteristic (PC) degradation. Different heterogeneous and homogeneous models are presented considering copula functions and different combinations of the most used stochastic processes in degradation analysis as marginal distributions. This is an important aspect to consider because the behaviour of the degradation of each PC may be different in its nature. As the joint distributions of the proposed models result in complex distributions, the estimation of the parameters of interest is performed via MCMC. A simulation study is performed to compare heterogeneous and homogeneous models. In addition, the proposed models are implemented to crack propagation data of two terminals of an electronic device, and some insights are provided about the product reliability under heterogeneous models.

Wood chile peppers stalks-plastic composite production.

Nowadays, most common wood fibers used to produce wood plastic composites are from Oak and pine trees, rice hull, recycled paper, pallets and post-industrial oak fiber, furniture waste, or cedar wood chips. This paper is focused on a new wood plastic composites based on pre-dried New Mexico red chile stems and leafs with high-density polyethylene. The specimens were manufactured following ASTM 638-9 standard using injection molding. A series of experiments were designed to investigate how wood fiber length, geometry, and water absorption affects the microcellular structure of the new sustainable material. The effects of proportions and particle size on the strength of the resulting blend were analyzed. It was found that larger particle sizes of wood fiber showed higher mechanical properties. The blending of additives in the new sustainable material has shown to increase proper fiber dispersion improving the strength of the material.

Analysis of the mechanical properties of wood-plastic composites based on agriculture Chili pepper waste

In this study, a new composite material conception based on polypropylene (PP) and wood particles obtained from agricultural waste was investigated. Specifically, stalks and leafs of chili plants of the region of New Mexico, Texas, USA were used as a plant-based reinforcing material. For this, a design of experiments (DOE) is proposed considering different percentages of PP, wood, fire retardant, coupling agent and UV stabilizer, different mesh sizes are also considered. Samples were obtained using an extrusion molding machine. A statistical analysis is proposed to obtain the optimal percentages of the components considered in the fabrication of the samples in order to enhance the mechanical and morphologic properties of this new composite. Results show that the yield stress, breaking stress and ultimate tensile stress can be maximized with a proportion of 3 to 1 (63,75% PP and 21,25% wood fiber) of PP and wood fiber.