Modeling Consecutive Failures of Repairable Systems, with Applications in Warranty Cost Analysis

<p>Most engineered systems are inclined to fail sometime during their lifetime. Many of these systems are repairable and not necessarily discarded and replaced upon failure. Unlike replacements, where the failed system is replaced with a new and identical system, not all repairs have an equivalent effect on the working condition of the system. Describing the effect of repairs is a requirement in modeling consecutive failures of a repairable system–at the very least, it is assumed that a repair simply returns the failed system to an operational state without affecting its working condition (i.e. the repair is minimal). Although this assumption simplifies the modeling process, it is not the most accurate description of the effect of repair in real situations. Often, along with returning a failed system to an operational state, repairs can improve the working condition of the system, and thus, increase its reliability which impacts on the rate of future failures of the system. Repair models provide a generalized framework for realistic modeling of consecutive failures of engineered systems, and have broad applications in fields such as system reliability and warranty cost analysis. The overall goal of this research is to advance the state of the art in modeling the effect of general repairs, and hence, failures of repairable systems. Two specific types of system are considered: (i) a system whose working condition initially improves with time or usage, and whose lifetime is modeled as a univariate random variable with a non-monotonic failure rate function; (ii) a system whose working condition deteriorates with age and usage, and whose lifetime is modeled as a bivariate random variable with an increasing failure rate function. Most univariate lifetime distributions used to model system lifetimes are assumed to have increasing failure rate functions. In such cases, modeling the effect of general repairs is straightforward– the effect of a repair can bemodeled as a possible decrease, proportional to the effectiveness of the repair, in the conditional intensity function of the associated failure process. For instance, a general repair can be viewed as the replacement of the failed system with an identical system at a younger age, so that the conditional failure intensity following the repair is lower than the conditional failure intensity prior to the failure. When the failure rate function is initially decreasing, specifically bathtub-shaped, general repair models suggested for systems with increasing failure rate functions can only be applied when initial repairs are assumed to be minimal. In this study, we propose a new approach to modeling the effect of general repairs on systems with a bathtub-shaped failure rate function. The effect of a general repair is characterized as a modification in the conditional intensity function of the corresponding failure process, such that the system following a general repair is at least as reliable as a system that has not failed. We discuss applications of the results in the context of warranty cost analysis and provide numerical illustrations to demonstrate properties of the models. Sometimes the failures of a system may be attributed to changes in more than one measure of its working condition– for instance, the age and some measure of the usage of the system (such as, mileage). Then, the system lifetime is modeled as a bivariate random variable. Most general repair models for systems with bivariate lifetime distributions involve reducing the failure process to a one-dimensional process by, for instance, assuming a relationship between age and usage or by defining a composite scale. Then, univariate repair models are used to describe the effect of repairs. In this study, we propose a new approach to model the effect of general repairs performed on a system whose lifetime is modeled as a bivariate random variable, where the distributions of the bivariate inter-failure lifetimes depend on the effect of all previous repairs and following a general repair, the system is at least as reliable as a system that has not failed. The lifetime of the original system is assumed to have an increasing failure rate (specifically, hazard gradient vector) function. We discuss applications of the associated failure process in the context of two-dimensional warranty cost analysis and provide simulation studies to illustrate the results. This study is primarily theoretical, with most of the results being analytic. However, at times, due to the intractability of some of the mathematical expressions, simulation studies are used to illustrate the properties and applications of the proposed models and results.</p>

Modeling Consecutive Failures of Repairable Systems, with Applications in Warranty Cost Analysis

<p>Most engineered systems are inclined to fail sometime during their lifetime. Many of these systems are repairable and not necessarily discarded and replaced upon failure. Unlike replacements, where the failed system is replaced with a new and identical system, not all repairs have an equivalent effect on the working condition of the system. Describing the effect of repairs is a requirement in modeling consecutive failures of a repairable system–at the very least, it is assumed that a repair simply returns the failed system to an operational state without affecting its working condition (i.e. the repair is minimal). Although this assumption simplifies the modeling process, it is not the most accurate description of the effect of repair in real situations. Often, along with returning a failed system to an operational state, repairs can improve the working condition of the system, and thus, increase its reliability which impacts on the rate of future failures of the system. Repair models provide a generalized framework for realistic modeling of consecutive failures of engineered systems, and have broad applications in fields such as system reliability and warranty cost analysis. The overall goal of this research is to advance the state of the art in modeling the effect of general repairs, and hence, failures of repairable systems. Two specific types of system are considered: (i) a system whose working condition initially improves with time or usage, and whose lifetime is modeled as a univariate random variable with a non-monotonic failure rate function; (ii) a system whose working condition deteriorates with age and usage, and whose lifetime is modeled as a bivariate random variable with an increasing failure rate function. Most univariate lifetime distributions used to model system lifetimes are assumed to have increasing failure rate functions. In such cases, modeling the effect of general repairs is straightforward– the effect of a repair can bemodeled as a possible decrease, proportional to the effectiveness of the repair, in the conditional intensity function of the associated failure process. For instance, a general repair can be viewed as the replacement of the failed system with an identical system at a younger age, so that the conditional failure intensity following the repair is lower than the conditional failure intensity prior to the failure. When the failure rate function is initially decreasing, specifically bathtub-shaped, general repair models suggested for systems with increasing failure rate functions can only be applied when initial repairs are assumed to be minimal. In this study, we propose a new approach to modeling the effect of general repairs on systems with a bathtub-shaped failure rate function. The effect of a general repair is characterized as a modification in the conditional intensity function of the corresponding failure process, such that the system following a general repair is at least as reliable as a system that has not failed. We discuss applications of the results in the context of warranty cost analysis and provide numerical illustrations to demonstrate properties of the models. Sometimes the failures of a system may be attributed to changes in more than one measure of its working condition– for instance, the age and some measure of the usage of the system (such as, mileage). Then, the system lifetime is modeled as a bivariate random variable. Most general repair models for systems with bivariate lifetime distributions involve reducing the failure process to a one-dimensional process by, for instance, assuming a relationship between age and usage or by defining a composite scale. Then, univariate repair models are used to describe the effect of repairs. In this study, we propose a new approach to model the effect of general repairs performed on a system whose lifetime is modeled as a bivariate random variable, where the distributions of the bivariate inter-failure lifetimes depend on the effect of all previous repairs and following a general repair, the system is at least as reliable as a system that has not failed. The lifetime of the original system is assumed to have an increasing failure rate (specifically, hazard gradient vector) function. We discuss applications of the associated failure process in the context of two-dimensional warranty cost analysis and provide simulation studies to illustrate the results. This study is primarily theoretical, with most of the results being analytic. However, at times, due to the intractability of some of the mathematical expressions, simulation studies are used to illustrate the properties and applications of the proposed models and results.</p>

On The Burr XII-Gamma Distribution: Development, Properties, Characterizations and Applications

We introduce a four-parameter lifetime model with flexible hazard rate called the Burr XII gamma (BXIIG) distribution. We derive the BXIIG distribution from (i) the T-X family technique and (ii) nexus between the exponential and gamma variables. The failure rate function for the BXIIG distribution is flexible as it can accommodate various shapes such as increasing, decreasing, decreasing-increasing, increasing-decreasing-increasing, bathtub and modified bathtub. Its density function can take shapes such as exponential, J, reverse-J, left-skewed, right-skewed and symmetrical. To illustrate the importance of the BXIIG distribution, we establish various mathematical properties such as random number generator, ordinary moments, generating function, conditional moments, density functions of record values, reliability measures and characterizations. We address the maximum likelihood estimation for the parameters. We estimate the adequacy of the estimators via a simulation study. We consider applications to two real data sets to prove empirically the potentiality of the proposed model.

Bayesian and Classical Inference under Type-II Censored Samples of the Extended Inverse Gompertz Distribution with Engineering Applications

In this article, we introduce a new three-parameter distribution called the extended inverse-Gompertz (EIGo) distribution. The implementation of three parameters provides a good reconstruction for some applications. The EIGo distribution can be seen as an extension of the inverted exponential, inverse Gompertz, and generalized inverted exponential distributions. Its failure rate function has an upside-down bathtub shape. Various statistical and reliability properties of the EIGo distribution are discussed. The model parameters are estimated by the maximum-likelihood and Bayesian methods under Type-II censored samples, where the parameters are explained using gamma priors. The performance of the proposed approaches is examined using simulation results. Finally, two real-life engineering data sets are analyzed to illustrate the applicability of the EIGo distribution, showing that it provides better fits than competing inverted models such as inverse-Gompertz, inverse-Weibull, inverse-gamma, generalized inverse-Weibull, exponentiated inverted-Weibull, generalized inverted half-logistic, inverted-Kumaraswamy, inverted Nadarajah–Haghighi, and alpha-power inverse-Weibull distributions.

Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution

The goal of this paper is to develop an optimal statistical model to analyze COVID-19 data in order to model and analyze the COVID-19 mortality rates in Somalia. Combining the log-logistic distribution and the tangent function yields the flexible extension log-logistic tangent (LLT) distribution, a new two-parameter distribution. This new distribution has a number of excellent statistical and mathematical properties, including a simple failure rate function, reliability function, and cumulative distribution function. Maximum likelihood estimation (MLE) is used to estimate the unknown parameters of the proposed distribution. A numerical and visual result of the Monte Carlo simulation is obtained to evaluate the use of the MLE method. In addition, the LLT model is compared to the well-known two-parameter, three-parameter, and four-parameter competitors. Gompertz, log-logistic, kappa, exponentiated log-logistic, Marshall–Olkin log-logistic, Kumaraswamy log-logistic, and beta log-logistic are among the competing models. Different goodness-of-fit measures are used to determine whether the LLT distribution is more useful than the competing models in COVID-19 data of mortality rate analysis.

A New Scale-Invariant Lindley Extension Distribution and Its Applications

A new scale-invariant extension of the Lindley distribution and its power generalization has been introduced. The moments and the moment-generating functions of the proposed models have closed forms. The failure rate, the mean residual life, and the α -quantile residual life functions have been explored. The failure rate function of these models accommodates increasing, bathtub-shaped, and increasing then bathtub-shaped forms. The parameters of the models have been estimated by the maximum likelihood method for the complete and right-censored data. In a simulation study, the efficiency and consistency of the maximum likelihood estimator have been investigated. Then, the proposed models were fitted to four data sets to show their flexibility and applicability.

Bounded Odd Inverse Pareto Exponential Distribution: Properties, Estimation, and Regression

In this paper, we introduce a new three-parameter distribution defined on the unit interval. The density function of the distribution exhibits different kinds of shapes such as decreasing, increasing, left skewed, right skewed, and approximately symmetric. The failure rate function shows increasing, bathtub, and modified upside-down bathtub shapes. Six different frequentist estimation procedures were proposed for estimating the parameters of the distribution and their performance assessed via Monte Carlo simulations. Applications of the distribution were illustrated by analyzing two datasets and its fit compared to that of other distributions defined on the unit interval. Finally, we developed a regression model for a response variable that follows the new distribution.

New Method for Generating New Families of Distributions

This article presents a new method for generating distributions. This method combines two techniques—the transformed—transformer and alpha power transformation approaches—allowing for tremendous flexibility in the resulting distributions. The new approach is applied to introduce the alpha power Weibull—exponential distribution. The density of this distribution can take asymmetric and near-symmetric shapes. Various asymmetric shapes, such as decreasing, increasing, L-shaped, near-symmetrical, and right-skewed shapes, are observed for the related failure rate function, making it more tractable for many modeling applications. Some significant mathematical features of the suggested distribution are determined. Estimates of the unknown parameters of the proposed distribution are obtained using the maximum likelihood method. Furthermore, some numerical studies were carried out, in order to evaluate the estimation performance. Three practical datasets are considered to analyze the usefulness and flexibility of the introduced distribution. The proposed alpha power Weibull–exponential distribution can outperform other well-known distributions, showing its great adaptability in the context of real data analysis.

A new Weibull Exponentiated Inverted Weibull Distribution for modelling positively-skewed data

An Exponentiated Inverted Weibull Distribution (EIWD) has a hazard rate (failure rate) function that is unimodal, thus making it less efficient for modeling data with an increasing failure rate (IFR). Hence, the need to generalize the EIWD in order to obtain a distribution that will be proficient in modeling these types of dataset (data with an increasing failure rate). This paper therefore, extends the EIWD in order to obtain Weibull Exponentiated Inverted Weibull (WEIW) distribution using the Weibull-Generator technique. Some of the properties investigated include the mean, variance, median, moments, quantile and moment generating functions. The explicit expressions were derived for the order statistics and hazard/failure rate function. The estimation of parameters was derived using the maximum likelihood method. The developed model was applied to a real-life dataset and compared with some existing competing lifetime distributions. The result revealed that the (WEIW) distribution provided a better fit to the real life dataset than the existing Weibull/Exponential family distributions.