Abstract: Sometimes the amount of time available for testing could be considerably less than the expected lifetime of the component. To overcome such a problem, there is the accelerated life-testing alternative aimed at forcing components to fail by testing them at much higher-than-intended application conditions. These models are known as acceleration models. One possible way to translate test results obtained under accelerated conditions to normal using conditions could be through the application of the “Maxwell Distribution Law.” In this paper we will apply a combined approach of a sequential life testing and an accelerated life testing to a low alloy high-strength steel component used in the construction of overpasses in Brazil. The underlying sampling distribution will be three-parameter Inverse Weibull model. To estimate the three parameters of the Inverse Weibull model we will use a maximum likelihood approach for censored failure data. We will be assuming a linear acceleration condition. To evaluate the accuracy (significance) of the parameter values obtained under normal conditions for the underlying Inverse Weibull model we will apply to the expected normal failure times a sequential life testing using a truncation mechanism. An example will illustrate the application of this procedure.
Abstract: In this paper we will develop further the sequential life test approach presented in a previous article by [1] using an underlying two parameter Inverse Weibull sampling distribution. The location parameter or minimum life will be considered equal to zero. Once again we will provide rules for making one of the three possible decisions as each observation becomes available; that is: accept the null hypothesis H0; reject the null hypothesis H0; or obtain additional information by making another observation. The product being analyzed is a new electronic component. There is little information available about the possible values the parameters of the corresponding Inverse Weibull underlying sampling distribution could have.To estimate the shape and the scale parameters of the underlying Inverse Weibull model we will use a maximum likelihood approach for censored failure data. A new example will further develop the proposed sequential life testing approach.
Abstract: In this paper, the test purpose will be to assess
whether or not the accelerated model proposed by Eyring will be able
to translate results for the shape and scale parameters of an
underlying Weibull model, obtained under two accelerating using
conditions, to expected normal using condition results for these
parameters. The product being analyzed is a new type of insulate
fluid, and the accelerating factor is the voltage stresses applied to the
fluid at two different levels (30KV and 40KV). The normal operating
voltage is 25KV. In this case, it was possible to test the insulate fluid
at normal voltage using condition. Both results for the two
parameters of the Weibull model, obtained under normal using
condition and translated from accelerated using conditions to normal
conditions, will be compared to each other to assess the accuracy of
the Eyring model when the accelerating factor is only the voltage
stress.
Abstract: In this paper we will develop further the sequential
life test approach presented in a previous article by [1] using an
underlying two parameter Weibull sampling distribution. The
minimum life will be considered equal to zero. We will again provide
rules for making one of the three possible decisions as each
observation becomes available; that is: accept the null hypothesis H0;
reject the null hypothesis H0; or obtain additional information by
making another observation. The product being analyzed is a new
type of a low alloy-high strength steel product. To estimate the shape
and the scale parameters of the underlying Weibull model we will use
a maximum likelihood approach for censored failure data. A new
example will further develop the proposed sequential life testing
approach.