Honggen Chen’s research while affiliated with Zhengzhou University and other places

Conventional redundancy allocation optimization model is usually measured by single performance. It meets difficulties in modeling the redundancy allocation optimization of multi-state system (MSS) with hierarchical performance requirements. This study proposes a generalized optimization model for MSS. This model concentrates on the redundancy allocation problem of the MSS with the inter-level dependent performances requirements. In the case of the minimum cost or maximum availability of the system, the aim of this model is to optimize system configuration, such as the economic numbers and versions for the multilevel heterogeneous components with known reliability and cost characteristics. Firstly, two algorithms to evaluate the system availability are introduced. A modified universal generation function (UGF) algorithm combining hierarchical operators is developed to evaluate the accurate availability for the system. Then the recursive algorithm (RA) is also used to obtain the lower and upper bounds of system availability. Secondly, compared with the traditional optimization model for the single level system, the proposed model for the hierarchical system has more decision variables which lead to difficult computation. Therefore, the genetic algorithm (GA) is applied to solve the redundancy allocation, especially the optimal numbers and versions of the different-level components simultaneously. Finally, a realistic power system verifies the correctness and validity of the suggested model. In conclusion, the results show that the above model tends to be more flexible and effective in the redundancy allocation optimization. Furthermore, this model helps the engineer in the reliability design optimization for the complex systems.

This paper develops a joint decision-making model approach to preventive maintenance and SPC (statistical process control) with delayed monitoring considered. The proposal of delayed monitoring policy postpones the sampling process till a scheduled time and contributes to six renewal scenarios of the production process, where maintenance actions are triggered by scheduled duration of prenentive maintenance or the alert of X ¯ chart for monitoring the shift of process mean resulted by deterioration of equipment. By analyzing the evolution of the system in different scenarios, a mathematical model is given to minimize the expected cost per unit time by optimizing values of five variables (scheduled duration without monitoring, scheduled duration of preventive maintenance, sample size, sampling interval and control limit). The results of a numerical example indicate that the hourly cost of the proposed model is lower than the model that delayed monitoring is not considered when the system has a low hazard rate during the early period. Finally, a sensitivity analysis is performed to demonstrate the effect of model parameters.

Accelerated life tests usually contain subsampling, which represents a restriction on randomization. The two-stage approach can deal with lifetime data from experiments with subsampling. However, this method did not introduce how to reduce the biases of estimators and compute confidence intervals of low percentiles. In this article, we build the model between percentile and stress factors, and obtain likelihood-based inference on percentile. In addition, we reduce the biases of estimators using an unbiasing factor method. Finally, we illustrate our method through a real example and compare with other methods via simulation study. The simulation results show that our proposed method is better in most cases.