The main objective of this research is to establish and solve a scheduling model for the degraded flow shop taking completion time and average device idle time as optimization objectives. Therefore, considering the interaction among schedule, production, maintenance and degradation, a mathematical model with completion time as well as average device idle time is constructed. Based on the NSGA-II algorithm, a local search strategy is first proposed to obtain junction points and sparse points according to the non-dominated sorted results. Subsequently, the comparative experiments are conducted utilizing NSGA-II-ALS, NSGA-II, and PSO algorithms. The optimal solution sets and Pareto diagrams of the three algorithms are obtained. The Gantt charts of the optimal solutions are drawn. Eventually, the multi-objective evaluation indexes of each algorithm are calculated and compared. Using statistical experiments and analysis, it can be found that NSGA-II-ALS exhibit excellent performance compared with other algorithms. The scheduling model proposed in this paper is able to effectively coordinate and balance the relationship among production, equipment maintenance and degradation, thus improving the efficiency of manufacturing systems, saving production costs, and realizing the optimal utilization of resources.

With the rapid development of oil and gas industry, as well as geological exploration industry, the requirements on properties of aluminum alloy drill pipes are increasing. During heat assembly of aluminum alloy drill pipes, the cooling process inside the pipes has a direct impact on the connection performance of pipes. Thus, study of the convective heat transfer coefficient between the cooling water and the internal wall of aluminum alloy pipes is important. Conventional algorithms cannot easily solve the problem of determining the heat transfer coefficient at the complex structure of aluminum alloy drill pipes. Therefore, this article conducts a heat assembly experiment between aluminum alloy drill pipes and steel joints to obtain adequate, accurate temperature data. Based on these experimental data and an inverse heat conduction model, the heat transfer coefficients during the heat assembly process are determined by a finite element program and the differential evolution algorithm. The correlation curve between the cooling water flowrate and the convective heat transfer coefficient obtained in this article is important in the accurate prediction of heat transfer capacity and temperature field distribution during heat assembly at different cooling water flowrates. The analysis results show that the heat transfer coefficients are nonlinear functions of cooling water flowrates. The temperature is highest at location A ₁ and gradually declines backward along the axis of the drill pipe. The heat transfer coefficient gradually declines backward along the axis of the drill pipe. The increasing flowrate of cooling water will cause the convective heat transfer coefficient along the axis of the drill pipe to escalate irregularly.

In order to model and evaluate the reliability of long-life high-voltage relays with small-sample fault data characteristics, a reliability analysis method integrating average rank, the minimum mean square distance empirical distribution function, and total least squares estimation is proposed. In the random truncation experiment, considering the influence of random truncation data, the average rank method is used to correct the rank of small-sample fault data; then, the optimal empirical distribution function for small-sample fault data is obtained through the minimum average square distance, which can overcome the impact of small-sample fault data randomness. Under the assumption of the Weibull distribution model, the total least squares estimation method is used for reliability model parameter estimations, and the linear correlation coefficient and d-test method are used for model hypothesis testing. If two or more distribution models pass the linear correlation coefficient test and the d-test simultaneously, the root mean square error and relative root mean square error are applied to determine the optimal reliability model. The effectiveness of this method is verified by comparing it with the maximum likelihood estimation method.