In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

Aiming at the scheduling problem of logistics distribution vehicles, an enhanced artificial electric field algorithm (SC-AEFA) based on the sine cosine mechanism is proposed. The development of the SC-AEFA was as follows. First, a map grid model for enterprise logistics distribution vehicle path planning was established. Then, an enhanced artificial electric field algorithm with the sine cosine mechanism was developed to simulate the logistics distribution vehicle scheduling, establish the logistics distribution vehicle movement law model, and plan the logistics distribution vehicle scheduling path. Finally, a distribution business named fresh enterprise A in the Fuzhou Strait Agricultural and Sideline Products Trading Market was selected to test the effectiveness of the method proposed. The theoretical proof and simulation test results show that the SC-AEFA has a good optimization ability and a strong path planning ability for enterprise logistics vehicle scheduling, which can improve the scheduling ability and efficiency of logistics distribution vehicles and save transportation costs.

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

In order to further reduce the aerodynamic drag of High-speed Electric Multiple Units (EMU), an active flow control drag reduction method combining air suction and blowing is proposed at the rear of the EMU train. A numerical calculation method based on realizable k-ε is used to investigate the aerodynamic drag characteristics of a three-car EMU with a speed of 400 km/h. The influence of different suction-blowing mass flow rates, the position and number of suction and blowing ports on the aerodynamic drag and surface pressure of the EMU tail are analyzed. The results demonstrate that suction and blowing at the tail reduce the pressure drag of EMU. And with the growth of air suction-blowing mass flow rate, the aerodynamic drag reduction rate of the tail car gradually increases, but the increment of drag reduction rate gradually decreases. Under the same mass flow rate of the suction and blowing, the closer the ports are to the upper and lower edges of the windscreen, the lower the pressure drag of the tail car is. At the same flow flux of air suction and blowing, the more the number of ports, the better the pressure drag reduction effect of the tail car. This study provides a reference for the next generation of EMU aerodynamic drag reduction and is of great significance for breaking through the limitations of traditional aerodynamic drag reduction.

This study explores the use of Multi-Objective Genetic Algorithm (MOGA) for thermodynamic characteristics of serrated plate-fin heat exchanger (PFHE) under numerical simulation method. Numerical investigations on the important structural parameters of the serrated fin and the j factor and the f factor of PFHE are conducted, and the experimental correlations about the j factor and the f factor are determined by comparing the simulation results with the experimental data. Meanwhile, based on the principle of minimum entropy generation, the thermodynamic analysis of the heat exchanger is investigated, and the optimization calculation is carried out by MOGA. The comparison results between optimized structure and original show that the j factor increases by 3.7%, the f factor decreases by 7.8%, and the entropy generation number decreases by 31%. From the data point of view, the optimized structure has the most obvious effect on the entropy generation number, which shows that the entropy generation number can be more sensitive to the irreversible changes caused by the structural parameters, and at the same time, the j factor is appropriately increased.

The influence of the improvement of the finned tube radiator unit structure on the fluid flow and heat transfer effect of the locomotive was studied. A saw-toothed fin structure with aluminum instead of copper was proposed to keep the position and size of the flat copper hot water pipe unchanged. CFD simulation analysis was carried out by ICEPAK17.0, under the conditions of an ambient temperature of 24.6 °C, atmospheric pressure of 85,040 Pa and air density ρ = 0.94 kg/m³, to compare the changes of velocity field, temperature field, turbulence field and field synergy angle. The sawtooth structure of the new heat sink increases the turbulence effect of the fluid, reduces the thickness of the outer boundary layer of the water pipe, and strengthens the heat transfer effect of the radiator. Finally, the baffle height, wing window width and sawtooth angle of the sawtooth structure were selected, and the heat transfer coefficient and pressure under three conditions of low, medium and high were used as indexes to analyze the influence of each parameter on the performance of the radiator. The results show that the heat dissipation effect of the serrated aluminum sheet is higher than that of the copper sheet, the heat transfer coefficient is increased by about 1.3%, the average pressure is reduced, the turbulence performance is improved, the synergy angle is reduced by about 2.3°, and the new radiator has better performance. The fin factor has the greatest influence on the heat transfer coefficient and the least influence on the pressure. When the baffle is about 0.15 mm high, the heat transfer coefficient is the largest, and the height change has the highest effect on the pressure. The included Angle factor has the least influence on the heat transfer effect, and the influence on the pressure is higher. By changing the fin window structure, the thermal performance of the finned tube radiator can be improved.