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Language: enSubject: Reliability Analysis

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    Estimation of Parameters for a System Equipped with Protection Block
    (Elsevier, 2026) Kus, Colkun; Eryilmaz, Serkan
    This paper studies the problem of estimating unknown parameters involved in a system which is equipped with a protection block. The system has different failure rates depending on whether the protection block is present or not, as the protection block is modeled by its own lifetime distribution and contributes an additional failure component to the system. The model is analyzed under the assumption of exponentially distributed lifetimes, leading to the study of its distributional properties and the estimation problem for its unknown parameters. Closed-form expressions for the maximum likelihood estimators are obtained. Furthermore, theoretical expectations and variances of the estimators are derived. We also discuss the stress-strength reliability estimation problem and construct confidence intervals for the associated reliability measure. Numerical results are provided to demonstrate the implementation of the proposed methods.
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    Robust Divergence-Based Tests of Hypotheses for Simple Step-Stress Accelerated Life-Testing Under Gamma Lifetime Distributions
    (Elsevier B.V., 2026) Balakrishnan, N.; Jaenada, M.; Pardo, L.
    Many modern devices are highly reliable, with long lifetimes before their failure. Conducting reliability tests under actual use conditions may require therefore impractically long experimental times to gather sufficient data for developing accurate inference. To address this, Accelerated Life Tests (ALTs) are often used in industrial experiments to induce product degradation and eventual failure more quickly by increasing certain environmental stress factors. Data collected under such increased stress conditions are analyzed, and results are then extrapolated to normal operating conditions. These tests typically involve a small number of devices and so pose significant challenges, such as interval-censoring. As a result, the outcomes are particularly sensitive to outliers in the data. Additionally, a comprehensive analysis requires more than just point estimation; inferential methods such as confidence intervals and hypothesis testing are essential to fully assess the reliability behaviour of the product. This paper presents robust statistical methods based on minimum divergence estimators for analyzing ALT data of highly reliable devices under step-stress conditions and Gamma lifetime distributions. Robust test statistics generalizing the Rao test and divergence-based tests for testing linear null hypothesis are then developed. These hypotheses include in particular tests for the significance of the identified stress factors and for the validity of the assumption of exponential lifetimes. © 2026