Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.

Rotating machine fault diagnosis plays a vital role in reducing maintenance costs and preventing accidents. Machine learning (ML) methods and Internet of things (IoT) technologies have been recently introduced into machine fault diagnosis and have generated inspiring results. An ML model with more trainable parameters can typically generate a higher fault diagnostic accuracy. However, the IoT nodes have limited computation and storage resources. How to design an ML model with high accuracy and computational efficiency is still a difficulty and challenge. This work develops an enhanced sparse filtering (ESF) method for mining and fusing the features of the machine signals for fault diagnosis. First, a dimension reduction algorithm is utilized for obtaining the principal components of the vibration signals that are hindered by noises. The distinct features of the principal components are then exploited by using sparse filtering (SF). To reduce the overfitting of the SF model, the L1/2 norm is applied to regularize the objective function. Finally, the obtained features are combined as the inputs of a softmax classifier for machine fault pattern recognition. The effectiveness, superiority, and robustness of the proposed ESF method are validated by the simulated signals and the practical bearing and motor fault signals compared with the other conventional methods. The lightweight and intelligent ESF algorithm is also deployed onto an edge computing node to realize online motor fault diagnosis. The designed model and the proposed method show great potential in highly accurate and efficient rotation machine fault diagnosis.

Aiming at the vibration signals of rolling bearings of a hot die forging press, which are nonlinear, unstable, and easily disturbed by strong background noise, a fault diagnosis method based on a short-time Fourier transform and optimized convolutional neural network was proposed. First, the Fourier transform was applied to the vibration signals of rolling bearings of a hot die forging press to obtain complete time-frequency samples. After spectrum compression, it is input into the convolutional neural network model. By optimizing activation function and adjusting network parameters, the purpose of efficient and fast detection in the case of small samples can be achieved. Through the simulation test, the feasibility and effectiveness are verified. The results show that the proposed method has high recognition accuracy for fault diagnosis of rolling bearings of hot die forging press.

The aluminium-matrix composites (AMCs) consisted of (5, 10 and 15) x/% SiC particles (SiCp) in an aluminium alloy 7055 matrix. Specimens were taken from hot-press sintering. High-strain-rate tests were performed using the split-Hopkinson pressure bar (SHPB) method. The microstructures were observed with a scanning electron microscope (SEM) to understand the damage mechanisms of the SiCp/7055 Al composites at high strain rate. The SHPB test results show that the SiCp-reinforced composites are more sensitive to strain rate than the unreinforced material. The strain-rate sensitivity of the flow stress of these composites increases substantially with the increase of the strain rate. The flow stress of SiCp/7055Al composites with 10 x/% and 15 x/% SiCp at 3000 s–1 first increases and then decreases with the increase of the plastic strains, which was caused by the heat generated during adiabatic compression. Microstructure-characterization results show that SiCp cracking and SiCp/7055Al interface debonding are the main damage mechanisms of the composites. The SiCp volume fraction and strain rate affect the damage of composites during the dynamic compressive deformation of the SiCp /7055Al composites.