Remaining useful life (RUL) prediction of rolling bearing plays an important role in maintaining the safety of the equipment. However, the data collected from industrial scene often contains noises, which affects the RUL prediction precision of rolling bearing. To overcome the above problem, a data-driven scheme for RUL prediction of rolling bearing is proposed based on convolutional denoising autoencoder (CDAE) and bidirectional long short-term memory network (Bi-LSTM). In the proposed method, the vibration signal is directly used as input of the prognostics network model. Then, a denoising network model based on CDAE is built to reduce the effect of noise. Through stacking the convolutional autoencoder, the noise component is automatically removed from the raw data. Finally, the network model based on Bi-LSTM is established to extract the high-dimensional degradation characteristics of bearing and estimate the RUL of the rolling bearing. The experimental results on the Xi’an Jiaotong University bearing dataset show that the proposed method has satisfied performance of RUL prediction.

The scatter of uncertain factors of the planetary roller screw mechanism, which originated from the manufacturing process, has a significant influence on its serving performance. However, this influence remains insufficiently studied. Further, the elastic–plastic contact analysis of the planetary roller screw mechanism with considering the uncertainties needs large numbers of repetitive calculations by traditional finite element method, which is labor intensive and often impractical. In this paper, an uncertain model of the planetary roller screw mechanism, including parameterization and finite element calculation, is established to conduct the elastic–plastic contact analysis automatically. Besides, the finite element method results can be also obtained automatically with the self-compiled flow program and the secondary development platform using the design of experiment method. Then, a mathematic model is applied to value the influence of the uncertain factors on the contact characteristics. The Pareto graphs are plotted to clearly show this sequence of the percent effects of all the uncertain factors. The present work, for the first time, developed an automatic modeling method for analyzing efficiently the uncertain factors of planetary roller screw mechanism, which is worthy in industrial application.

Reliability-based design optimization (RBDO) has become a prevalent design for aeronautical and aerospace engineering. The main problem is that it is impractical in complex cases with multi-failure regions, especially in multi-objective optimization. The active learning method can obtain an adaptive size of samples to get a relatively acceptable accuracy. The problem of RBDO using the traditional active learning Kriging (ALK) method is that the design space is generally still and only one training point is selected, which is not reasonable based on the concept of importance sampling and parallel calculation. As a consequence, the accuracy improvement is limited. In this paper, we investigate the method of obtaining an optimal size of design and reliability to assess space in parallel, simultaneously. A strategy of parallel adaptive candidate (PAIC) region with ALK is proposed and a sequential optimization and reliability assessment (SORA) method is modified to efficiently improve the accuracy. Importance sampling is used as a demonstration for the modified SORA with more accuracy. The method is then verified using mathematical cases and a scooping system of an amphibious aircraft.

With a higher operation temperature, the conventional aero-turbine single web disk (SWD) has reached its limits. The twin-web disk (TWD) has been designed as a breakthrough, which has an expected performance in weight loss, strength and heat transfer efficiency. However, the lack of investigation on the position of the cooling air inlet is slowing down further application of TWD. Therefore, for a further study, inlet position optimization with maximum average Nusselt number is conducted for TWD flow structure study. The average Nusselt number result shows that the TWD has a better performance in heat transfer. All the works, including modeling and analyzing, can be referred for engineering design. And the conclusions obtained in this paper could be valuable for the future improvement of the TWD.