In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive maintenance, this paper proposes a novel methodology of integrating the preventive maintenance scenarios into assembly line balancing problems to bypass the unavailable workstation. A lexicographic model is formulated to generate multiple task assignment plans that ensure primarily the high productivity under regular operation scenario and guarantee secondarily the production continuity under preventive maintenance scenarios. And, an improved whale optimization algorithm (IWOA) with three modifications is proposed to solve this problem. Specifically, a combined crossover operator enhances better combination in exploration; three best search agents promote the exploitation; partial regeneration avoids being trapped in local optima. More than five thousand experiments demonstrate that the joint of three modifications endows the IWOA significant superiority over six variants and other six well-known algorithms. Moreover, integrating preventive maintenance scenarios into the assembly line balancing problem increases the production efficiency by 1% at the cost of small production adjustment.

Leakage test is one of the acceptance tests to ensure hydraulic cylinder reliability. Under the consideration of economic and technical maturity conditions, the existing internal leakage detection methods may be difficult to achieve rapid screening. After research, the pressure decay test method is a common method of quickly testing hydraulic cylinders for internal leakage in the manufacturing industry. However, the validity of the pressure decay test method has not been theoretically verified, limiting the versatility of the method. This paper will theoretically validate the effectiveness of the pressure decay test method and provide important theoretical support for its use as a general rapid screening method for internal leakage, thereby improving the reliability and safety of hydraulic systems. Both simulation and experimental results show that there is a linear relationship between the pressure decay rate and the internal leakage rate. Especially for working pressures greater than 16 MPa, the pressure factor has an insignificant impact on the application of this method.

Owning to the advantage of keeping the operating environment safe, high reliability, and low production cost, predictive maintenance has been widely used in industry and academia. Predictive maintenance based on degeneration state mainly studies the degeneration prediction. However, on account of the error of the sensor and human, condition monitoring data may not directly reflect the true degeneration. The degeneration model with dynamic explanatory covariates which is named as proportional hazard model is proposed to deal with the semi-observed monitoring condition. And the degeneration prediction mainly adopts a single prediction model, which leads to low prediction accuracy. A combination forecasting model can effectively solve the above problem. Compared to the traditional prediction method, the neural network model can use the “black box” characteristic to indirectly construct the degeneration model without complex mathematical derivation. Therefore, we propose a combination BP-RBF-GRNN neural network model which is applied to improve the degeneration prediction with dynamic covariate. Based on the above two aspects, a predictive maintenance optimization framework based on the proportional hazard model and BP-RBF-GRNN neural network model is proposed to improve maintenance efficiency and reduce maintenance costs. The simulation results of thrust ball bearing show that the proposed method can effectively improve the degeneration prediction accuracy and reduce the maintenance cost rate to a certain extent.

Enhancing energy-efficient measures is essential for reducing carbon emissions. Degradation testing is a crucial energy-efficient technology in reliability testing, particularly when evaluating long-life, high-reliability products. This method frequently employs time-censored approaches to improve energy efficiency. However, the time-censored method complicates the determination of the termination time, as changes in the time-censored point lead to opposing variations in testing efficiency and reliability evaluation accuracy. While some studies have accelerated the acquisition of failure time data by tightening thresholds, these methods have restricted applicability and overlook valuable degradation data. To address these limitations and fully utilize degradation information, this paper proposes a novel reliability testing method that employs degradation testing with tightened threshold censoring. Instead of using test time as the censoring criterion, this method employs the product’s performance characteristic quantity. The Wiener process is utilized for degradation modeling and remaining useful life prediction. Subsequently, hypothesis testing is utilized to identify the failure time distribution appropriate for the object under study. The effectiveness of the proposed method was verified through a simulation study of the return oil flow in piston pumps and a case study of solenoid directional valves. The results demonstrate that this method significantly improves energy efficiency and exhibits enhanced stability compared to the time-censored method. Moreover, the research findings provide valuable guidance for selecting appropriate tightened thresholds when conducting reliability evaluation using censored degradation tests for similar products.