The service life assessment of previous fatigue stress has a fundamental role in estimating the reliability of products like fiber optical connectors, and should replicate use in severe environments in telecommunications systems. However, the vibration prediction models used in the literature present limitations in vibration life prediction. In this paper, we propose using the insertion loss fatigue life and including it in the Weibull distribution to determine the Weibull parameters ƞ and β to evaluate fiber optic connector reliability R(t) under environmental and mechanical testing stress. We analyzed the failure data of a standard telecommunication fiber optical connector under a program of service life stress testing against that of a fiber optical connector under only mechanical vibration stress testing. The fiber connectors were monitored during the vibration testing to review the transient change of the optical signal. Their results showed that the reliability of the fiber connectors submitted to the service life program was $R\left(t\right)=0.694$, while that of the fiber connectors submitted to mechanical vibration only was $R\left(t\right)=0.970$. In addition, the analysis showed that the service life testing consumed 70.2% of the product’s lifetime. We present in Numerical Validation the steps to determine the acceleration factors of the vibration test developed.

In the paper, the formulated Weibull vibration reliability methodology is based on the cumulative vibration damage analysis. It lets us determine the reliability index of each damage element, each damage block, and the reliability of the analyzed element. The vibration cumulated damage is cumulated until D=1, by using the addressed vibration stress and a nonlinear cumulative damage model. Based on static and modal analysis, the vibration stress is determined by incorporating to it the geometry, weight and resonance effects. In the reliability analysis the Weibull shape (β) parameter is determined directly from the number of blocks, for which D=1. The damage element, damage block, and element reliability are all determined based on the values of beta (β) and D_i elements. Finally, based on the cumulated applied cycles n_i the Weibull scale (η) parameter is determined by to each D_i elements, damage block, and analyzed element.

In this paper, based on the Weibull Inverse Power Law, we present a methodology to determine the following: (1) the failure percentiles, referred to as the P–S–N field, of an S–N curve for a 42CrMo4 steel material exhibiting bilinear ($s_1 \mathrm{and} s_2)$ behavior (e.g., a competence failure mode); (2) the Weibull family that characterizes the entire bilinear behavior; and (3) the zero-vibration test plan that meets the required vibration reliability index of $R\left(t\right)=0.97$ with a reliability confidence level of $CL=0.75$. From the application, based on the formulated normal–Weibull relationship, we determine the failure percentiles for the normal (one, two, and three) sigma levels, as well as those failure percentiles corresponding to the capability ($Cp$) and ability ($Cpk$) indices. Finally, we present the formulation to determine the $R\left(t\right)$ index and the $CL$ level associated with each normal percentile, along with their numerical values.

In this paper, the method to determine the percentiles of the P-S-N field when it presents bilinear (s1 and s2) behavior (e.g competence failure mode) is given. The percentiles are determined around the Weibull scale (η) parameter in Cycles that corresponds to the median applied stress of the whole bilinear behavior. The reliability (R(t)) index that corresponds to each P-S-N percentile are also determined. As well as the confidence reliability percentile (CL) that corresponds to each P-S-N percentile. The formulation to relate CL with its corresponding normal P-S-N percentile is also given. Numerically, the percentiles of the normal (one, two, and three) sigma levels, as well as those that correspond to the capability Cp and Cpk indices are related with their CL values, and R(t) indices. To validate the Weibull cycle bilinear family, a test plan is designed and numerically performed. In the numerical application we use the 42CrMo4 steel material.

In this paper, based on the cumulated damage generated by the cyclical application of the testing vibration profile, a methodology to determine the reliability index of the analyzed component is given. In the analysis, the testing time, component geometry, applied weight, and material resonant frequency are all of them considered by using a dynamic, and amplitude factor. Then, from the generated vibration response data of the analysis, the corresponding bending stress is determined for each one of the rows of the testing profile. And by the cyclical application of the whole testing profile, the corresponding damage is cumulated to each profile cycle until the cumulated damage is D=1. Finally, the reliability that each one of the cumulated blocks (cyclical profile application) is determined by considering, the random vibration is a stress variable. Thus, we use the cumulative damage model with the inverse power law stress/life relationship to determine the two parameter Weibull distribution used to estimate the reliability index of each cumulated block. As a numerical application, we analyze a cantilever aluminum beam Al 6061-T6.

La predicción de confiabilidad de productos electrónicos es una actividad fundamental en la industria automotriz por diferentes razones: 1) entender si se cumple con un objetivo de confiabilidad, 2) comparar entre diseños alternativos, o 3) evaluar mejoras de confiabilidad. La predicción de confiabilidad queda definida por el cálculo de la taza de falla de todos los componentes electrónicos que constituyen un sistema/producto. En el campo automotriz existen diferentes guías diseñadas para la predicción de confiabilidad de componentes electrónicos, donde el estándar Siemens SN 29500 es bien aceptado en la industria automotriz. Sin embargo, el estándar Siemens SN 29500, así como otros estándares, da las bases para el cálculo de la taza de falla asumiendo condiciones ambientales constantes, pero no muestra un proceso paso a paso cuando el producto opera en el campo bajo diferentes condiciones ambientales. De esta manera, en este artículo se presenta un proceso paso a paso, para el entendimiento de la implementación del estándar Siemens SN 29500, cuando las condiciones ambientales no son constantes, para obtener un valor de taza de falla/confiabilidad de un producto, siguiendo una aplicación electrónica automotriz.