In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

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.

The reliable operation of IGBT (insulated gate bipolar transistor) is the key to the performance of motor control system, which directly affects the performance of the vehicle. By contacting with the surface of the radiator, the heat device rapidly transfers heat to the fin, which is then absorbed by the high-speed airflow between the fins and taken away to form a cooling channel to ensure that the heat device is always at an appropriate temperature during work. The power loss of IGBT under rated working conditions was estimated theoretically. The temperature field of the radiator under typical working conditions was simulated by ICEPAK, and the radiator was parameterized. The simulation results show that under the condition of forced cooling and heat dissipation, the optimized radiator meets the heat dissipation requirements of the motor controller.

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.