Schedule Planning and Maintenance Activities Auxiliary Power Unit (APU) Boeing 737-500 Aircraft With Reliability Method

Penelitian ini bertujuan untuk merencanakan jadwal dan aktifitas maintenance yang yangefektif pada sistem auxiliary power unit sehingga tidak terjadi lagi kegagalan ataupun kerusakan yang tidak di rencanakan atau terjadi secara tiba – tiba. Kegagalan pada peralatan auxiliary power unit ada sering terjadi pada beberapa sistem kerja yaitu electrical system, Lubrication System dan Ignition System, di mana hal ini menimbulkan kerugian yang cukup besar bagi perusahaan penerbangan. Metode penelitian ini menggunakan pendekatan kualitatif dan kuantitatif, analisis kualitatif menggunakan metode Failure Mode Effect and Critically Analysis (FMECA) dengan menganalisis faktor – faktor penyebab kegagalan dan efek terjadinya kegagalan, dengan hasil penyebab kegagalan pada beberapa sitem kerja auxiliary power unit (APU) adalah sebagai berikut electrical system adalah pada komponen start Relay, Lubrication System adalah pada komponen Oil Filter, Ignition System adalah pada igniter plug. Dari hasil analisis FMECA tersebut di lakukan analisis kuantitatif dengan analisis dilakukan menggunakan metode reliability, parameter kehandalan dihitung dengan probabilitas distribusi Weibull, untuk menentukan batas kritis waktu operasional komponen ataupun part sistem yang merupakan batas kehandalan suatu sistem auxiliary power unit. Batas kritis operasional electrical system adalah sebesar 434 jam terbang, lubrication system adalah 1186 jam terbang, dan Ignition system adalah sebesar 1610 jam terbang, selanjutnya hasil tersebut di gunakan untuk menentukan jadwal maintenance yang efektif di dukung dengan perencanaan aktifitas maintenance yang tepat untuk menghilangkan penyebab – penyebab kegagalan pada peralalatan auxiliary power unit.

A New Extended Cosine—G Distributions for Lifetime Studies

In this article, we introduce a new extended cosine family of distributions. Some important mathematical and statistical properties are studied, including asymptotic results, a quantile function, series representation of the cumulative distribution and probability density functions, moments, moments of residual life, reliability parameter, and order statistics. Three special members of the family are proposed and discussed, namely, the extended cosine Weibull, extended cosine power, and extended cosine generalized half-logistic distributions. Maximum likelihood, least-square, percentile, and Bayes methods are considered for parameter estimation. Simulation studies are used to assess these methods and show their satisfactory performance. The stress–strength reliability underlying the extended cosine Weibull distribution is discussed. In particular, the stress–strength reliability parameter is estimated via a Bayes method using gamma prior under the square error loss, absolute error loss, maximum a posteriori, general entropy loss, and linear exponential loss functions. In the end, three real applications of the findings are provided for illustration; one of them concerns stress–strength data analyzed by the extended cosine Weibull distribution.

A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.

Reliability and Inference for Multi State Systems: The Generalized Kumaraswamy Case

Semi-Markov processes are typical tools for modeling multi state systems by allowing several distributions for sojourn times. In this work, we focus on a general class of distributions based on an arbitrary parent continuous distribution function G with Kumaraswamy as the baseline distribution and discuss some of its properties, including the advantageous property of being closed under minima. In addition, an estimate is provided for the so-called stress–strength reliability parameter, which measures the performance of a system in mechanical engineering. In this work, the sojourn times of the multi-state system are considered to follow a distribution with two shape parameters, which belongs to the proposed general class of distributions. Furthermore and for a multi-state system, we provide parameter estimates for the above general class, which are assumed to vary over the states of the system. The theoretical part of the work also includes the asymptotic theory for the proposed estimators with and without censoring as well as expressions for classical reliability characteristics. The performance and effectiveness of the proposed methodology is investigated via simulations, which show remarkable results with the help of statistical (for the parameter estimates) and graphical tools (for the reliability parameter estimate).

Reliability Estimation in Multidimensional Scales: Comparing the Bias of Six Estimators in Measures With a Bifactor Structure

In the context of multidimensional structures, with the presence of a common factor and multiple specific or group factors, estimates of reliability require specific estimators. The use of classical procedures such as the alpha coefficient or omega total that ignore structural complexity are not appropriate, since they can lead to strongly biased estimates. Through a simulation study, the bias of six estimators of reliability in multidimensional measures was evaluated and compared. The study is complemented by an empirical illustration that exemplifies the procedure. Results showed that the estimators with the lowest bias in the estimation of the total reliability parameter are omega total, the two versions of greatest lower bound (GLB) and the alpha coefficient, which in turn are also those that produce the highest overestimation of the reliability of the general factor. Nevertheless, the most appropriate estimators, in that they produce less biased estimates of the reliability parameter of the general factor, are omega limit and omega hierarchical.

Markov Chain Monte Carlo-Based Estimation of Stress–Strength Reliability Parameter for Generalized Linear Failure Rate Distributions

This paper provides Bayesian and classical inference of Stress–Strength reliability parameter, [Formula: see text], where both [Formula: see text] and [Formula: see text] are independently distributed as 3-parameter generalized linear failure rate (GLFR) random variables with different parameters. Due to importance of stress–strength models in various fields of engineering, we here address the maximum likelihood estimator (MLE) of [Formula: see text] and the corresponding interval estimate using some efficient numerical methods. The Bayes estimates of [Formula: see text] are derived, considering squared error loss functions. Because the Bayes estimates could not be expressed in closed forms, we employ a Markov Chain Monte Carlo procedure to calculate approximate Bayes estimates. To evaluate the performances of different estimators, extensive simulations are implemented and also real datasets are analyzed.

Estimating Stress-Strength Model for Weighted Lomax Distribution

This paper aims to estimate the stress-strength reliability parameter when and are follow the weighted Lomax (WL) distribution. The behavior of stress-strength parameters and reliability have been studied by using maximum likelihood and Bayesian estimators through the Monte Carlo simulation study which carried out showing satisfactory performance of the estimators obtained. Finally, two real data sets representing waiting times before service of the customers of two banks A and B are fitted using the WL distribution and used to estimate the stress-strength parameters and reliability function.

Approximation of continuous random variables for the evaluation of the reliability parameter of complex stress–strength models

In many management science or economic applications, it is common to represent the key uncertain inputs as continuous random variables. However, when analytic techniques fail to provide a closed-form solution to a problem or when one needs to reduce the computational load, it is often necessary to resort to some problem-specific approximation technique or approximate each given continuous probability distribution by a discrete distribution. Many discretization methods have been proposed so far; in this work, we revise the most popular techniques, highlighting their strengths and weaknesses, and empirically investigate their performance through a comparative study applied to a well-known engineering problem, formulated as a stress–strength model, with the aim of weighting up their feasibility and accuracy in recovering the value of the reliability parameter, also with reference to the number of discrete points. The results overall reward a recently introduced method as the best performer, which derives the discrete approximation as the numerical solution of a constrained non-linear optimization, preserving the first two moments of the original distribution. This method provides more accurate results than an ad-hoc first-order approximation technique. However, it is the most computationally demanding as well and the computation time can get even larger than that required by Monte Carlo approximation if the number of discrete points exceeds a certain threshold.