Development of remaining life estimation for MV PILC due to electrical stress using statistical method

Combined with the actual project, a grating ruler accelerated life test device is designed, which can simulate the actual loads, including temperature, humidity and speed stress. An accelerated life test scheme based on stepped stress loading is proposed, and 6 grating rulers are tested based on time-censored test method. An accelerated life model based on Weibull distribution used for evaluating the lifetime is established. Related experimental techniques also can be typical application cases for innovative practical teaching.

In addition to conventional censoring schemes such as Type-I or Type-II censoring, progressively censoring is a useful method to reduce cost and obtain additional reliability information in accelerated life testing. Statistical inference for accelerated life testing (ALT) with Weibull distribution and progressively censoring is found to be difficult. This paper develops generalized fiducial inference techniques for the constant-stress ALT model with Weibull distribution and progressively Type-II censoring. Simulation studies reveal the good performance of the proposed inference methods. An example is given for illustration.

It is difficult to verify the reliability of a new type of ammunition components through traditional statistical testing methods. And the accelerated life test of ammunition components at the present often requires a large amount of sample size. The calculation model for minimum sample size of the Inertial Gyro has been established by clearing the accelerated life test method of Inertial Gyro, considering the Weibull distribution is assumed to product life, and taking advantage of a priori information. And the minimum sample size of the Inertial Gyro that step-stress accelerated life-test required has been calculated out.

Lifetime data with covariates (e.g., temperature, humidity, and electric current) are frequently seen in science and engineering. An important example is accelerated life testing (ALT) data. In ALT, test units of a product are exposing to severer-than-normal conditions to expedite product failure. The resulting lifetime and/or censoring data with covariates are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This paper develops a new method for modeling lifetime data with covariates using phase-type (PH) distributions and a general life-stress relationship formulation. A numerical study is presented to compare the performance of this method with a mixture of Weibull distributions model. This general method creates a new direction for modeling and analyzing lifetime data with covariates for situations where the data-generating mechanisms are unknown or difficult to analyze using existing parametric ALT models and statistical tools.

Pneumatic cylinders, as a long lifetime product, are used continuously under normal conditions. So Accelerated Life Testing is an important tool to get information quickly on lifetime which is achieved by subjecting the test cylinders to conditions that are more severe than the normal ones. This paper firstly analyze the failure mechanism of pneumatic cylinder, then based on the General Log-Linear relationship and Weibull distribution, describes a model for multiple stress-type accelerated life data analysis. The paper gives the parameter statistical method of the mode by using likelihood theory. Based on the test data from ALT of pneumatic cylinders which adopt four test stresses including air temperature, motion frequency, working pressure and operation velocity, the parameter of the model are fitted. According to the test data and the estimated model, the paper extrapolates the pneumatic cylinder’s lifetime information under the normal user level.

Reliability assessment of SMD tantalum capacitors for Active Implantable Medical Devices: A methodology based on ALT and DoE

Accelerated life tests are used to explore the lifetime of extremely reliable items by subjecting them to elevated stress levels from stressors to cause early failures, such as temperature, voltage, pressure, and so on. The alpha power inverse Weibull (APIW) distribution is of great significance and practical applications due to its appealing characteristics, such as its flexibilities in the probability density function and the hazard rate function. We analyze the step stress partially accelerated life testing model with samples from the APIW distribution under adaptive type II progressively hybrid censoring. We first obtain the maximum likelihood estimates and two types of approximate confidence intervals of the distributional parameters and then derive Bayes estimates of the unknown parameters under different loss functions. Furthermore, we analyze three probable optimum test techniques for identifying the best censoring under different optimality criteria methods. We conduct simulation studies to assess the finite sample performance of the proposed methodology. Finally, we provide a real data example to further demonstrate the proposed technique.

Optimal design of accelerated life tests for the Weibull distribution under periodic inspection and type I censoring

The paper describes an investigation into aging of medium voltage mica-based groundwall insulation when subjected to high frequency AC and steep-front pulse voltages. The test conditions adopted in this study involve 60 Hz, 3 kHz and 4 kHz AC frequencies, nanosecond rise time pulses with repetition rates up to 2000 pps, and at temperatures of 20 and 155 /spl deg/C. The experimental data of time-to-failure in the accelerated life tests are presented by Weibull distributions. The parameters of the life model are developed that link the failure time to the amplitude and temperature of the applied voltages. The test method and the data processing adopted in this study are useful to characterize groundwall insulation in terms of improved vacuum pressure impregnation (VPI) and for applications at higher temperatures and frequencies.

This article describes Bayesian methods for accelerated life test planning with one accelerating variable, when the acceleration model is linear in the parameters, based on censored data from a log-location-scale distribution. We use a Bayesian criterion based on estimation precision of a distribution quantile at a specified use condition to find optimum test plans. We also show how to compute optimized compromise plans that satisfy practical constraints. A large-sample normal approximation provides an easy-to-interpret yet useful simplification to this planning problem. We present a numerical example using the Weibull distribution with type I censoring to illustrate the methods and to examine the effects of the prior distribution, censoring, and sample size. The general equivalence theorem is used to verify that the numerically optimized test plans are globally optimum. The resulting optimum plans are also evaluated using simulation.

In this paper, the Jeffreys priors for the step-stress partially accelerated life test with Type-II adaptive progressive hybrid censoring scheme data are considered. Given a density function family satisfied certain regularity conditions, the Fisher information matrix and Jeffreys priors are obtained. Taking the Weibull distribution as an example, the Jeffreys priors, posterior analysis and its permissibility are discussed. The results, which present that how the accelerated stress levels, censored size, hybrid censoring time and stress change time etc. affect the Jeffreys priors, are obtained. In addition, a theorem which shows there exists a relationship between single observation and multi observations for permissible priors is proved. Finally, using Metroplis with in Gibbs sampling algorithm, these factors are confirmed by computing the frequentist coverage probabilities.

Step‐stress accelerated life testing is often used to estimate the fatigue life or reliability of structural components. Various methods exist for designing the optimal accelerated life test and estimating the parameters of the fatigue‐life or reliability models. Although they are based on rigorous mathematical formulations, they sometimes result in inappropriate estimates of the model parameters if the experimental data consists of censored data. This article presents a robust engineering procedure for estimating the parameters of the fatigue‐life curve and its scatter, which combines an inverse power law and the two‐parametric Weibull distribution. The presented methodology is tested on use cases that stem from the experimental fatigue‐life data. The results show that the proposed approach can yield a good estimate of the fatigue‐life model. Moreover, it can be further concluded that the sensitivity of the fatigue‐life model to the type I censored data strongly depends on the data scatter.

Designing an accelerated degradation experiment with a reciprocal Weibull degradation rate

This paper presents a life-stress methodology that models the failure rate in the form of a bathtub curve. The model consists of the Alpha Power Transformation (APT), which adds an extra parameter to the probability distributions to achieve better flexibility in the representation in the data analysis. To build the life–stress relationship, the APT is combined with the Weibull Distribution (WD) and the Inverse Power Law (IPL) as a stress model to relate the data from the accelerated life tests (ALT), thus presenting the APTW-IPL. Statistical properties of the APTW-IPL are analyzed and discussed. For the parameter estimation of APTW-IPL, the Maximum Likelihood Estimator was used. On the other hand, to test the efficacy of the APTW-IPL, the model is compared with other methodologies that describe the behavior of the bathtub curve in two case studies related to determining the behavior of electronic devices that were subjected to ALT. The results show that the APTW-IPL can be a good option for reliability analysis in electronic devices. It represents the failure times in the form of a bathtub curve, the value of MTTF, and fitting the distribution to the case study data.

This paper describes Bayesian methods for life test planning with Type II censored data from a Weibull distribution, when the Weibull shape parameter is given. We use conjugate prior distributions and criteria based on estimating a quantile of interest of the lifetime distribution. One criterion is based on a precision factor for a credibility interval for a distribution quantile and the other is based on the length of the credibility interval. We provide simple closed form expressions for the relationship between the needed number of failures and the precision criteria. Examples are used to illustrate the results.