Recently, the two-parameter Chen distribution has widely been used for reliability studies in various engineering fields. In this article, we have developed various statistical inferences on the composite dynamic system, assuming Chen distribution as a baseline model. In this dynamic system, failure of a component induces a higher load on the surviving components and thus increases component hazard rate through a power-trend process. The classical and Bayesian point estimates of the unknown parameters of the composite system are obtained by the method of maximum likelihood and Markov chain Monte Carlo techniques, respectively. In the Bayesian framework, we have used gamma priors to obtain Bayes estimates of unknown parameters under the squared error and generalized entropy loss functions. The interval estimates of the baseline reliability function are obtained by using the Fisher information matrix and Bayesian method. A parametric hypothesis test is presented to test whether the failed components change the hazard rate function. A compact simulation study is carried out to examine the behavior of the proposed estimation methods. Finally, one real data analysis is performed for illustrative purposes.

中文翻译：

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基于序贯统计的Chen模型动力系统可靠性分析

最近，二参数陈分布已被广泛用于各个工程领域的可靠性研究。在本文中，我们假设 Chen 分布作为基线模型，对复合动力系统进行了各种统计推断。在这个动态系统中，组件的故障会给幸存的组件带来更高的负载，从而通过功率趋势过程增加组件的危险率。复合系统未知参数的经典点估计和贝叶斯点估计分别通过最大似然法和马尔可夫链蒙特卡罗技术得到。在贝叶斯框架中，我们使用伽马先验来获得平方误差和广义熵损失函数下未知参数的贝叶斯估计。利用Fisher信息矩阵和贝叶斯方法获得基线可靠性函数的区间估计。提出了一个参数假设检验来检验失效组件是否改变了危险率函数。进行了紧凑的模拟研究以检查所提出的估计方法的行为。最后，出于说明目的，进行了一项真实数据分析。