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.

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

In this paper, a new fractional-order (FO) PIλDµ controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order λ and the differential order μ are introduced in the controller, and the two additional adjustable factors make the FO PIλDµ controller have better accuracy and robustness. Simulations are carried out for comparison with a ship’s digital PID autopilot. The results show that the FO PIλDµ controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO PIλDµ controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO PIλDµ controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO PIλDµ controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.

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Fractional-Order Controller for Course-Keeping of Underactuated Surface Vessels Based on Frequency Domain Specification and Improved Particle Swarm Optimization Algorithm

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Bearing is an important component in mechanical equipment. Its main function is to support the rotating mechanical body and reduce the friction coefficient and axial load. In the actual operating environment, the bearings are affected by complex working conditions and other factors. Therefore, it is very difficult to effectively obtain data that meets the conditions of independent and identical distribution of training data and test data, which result in unsatisfactory fault diagnosis results. As a transfer learning method, joint distribution adaptive (JDA) can effectively solve the learning problem of inconsistent distribution of training data and test data. In this paper, a new bearing fault diagnosis method based on JDA and deep belief network (DBN) with improved sparrow search algorithm (CWTSSA), namely JACADN is proposed. In the JACADN, the JDA is employed to carry out feature transfer between the source domain samples and target domain samples, that is, the source domain samples and target domain samples are mapped into the same feature space by the kernel function. Then the maximum mean difference is used as the metric to reduce the joint distribution difference between the samples in the two domains. Aiming at the parameter selection of the DBN, an improved sparrow search algorithm (CWTSSA) with global optimization ability is used to optimize the parameters of the DBN in order to construct an optimized DBN model. The obtained source domain samples and target domain samples are divided into training set and test set, which are input the optimized DBN to construct a bearing fault diagnosis model for improving the diagnosis accuracy. The effectiveness of the proposed method is verified by vibration data of QPZZ-II rotating machinery. The experimental results show that the proposed JACADN method can effectively improve the fault diagnosis accuracy of rolling bearings under variable operating conditions.

Bearing fault diagnosis using transfer learning and optimized deep belief network

Parametrized time-frequency analysis (PTFA) can effectively improve time-frequency energy aggregation of non-stationary signal and immunity of cross term interference, but it exists the energy diffusion near the real instantaneous frequency. The improved multi-synchrosqueezing transform (IMSST) can improve the time-frequency energy aggregation, but it still has defects in processing strong FM and AM signals under noise interference. Therefore, in order to make use of their advantages and overcome their disadvantages, a novel parametrized multi-synchrosqueezing transform method based on weighted least square, IMSST and PTFA, namely PMSST is proposed in this paper. In the PMSST, the IMSST is designed to obtain the signal time-frequency representation with high energy aggregation. Then the ridge extraction algorithm is employed to extract the instantaneous frequency ridges of each mono-component signal. The weighted least square method is used to estimate the parameters of parameterized transform kernel. Finally, time-frequency spectrum is superimposed to obtain the time-frequency energy representation of the enhanced signal. The experiment results show that the PMSST can effectively process non-stationary signals with varying instantaneous frequency by the simulated signal and actual fault signals. 

Feature Extraction Using Parameterized Multisynchrosqueezing Transform

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.

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

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.

https://www.mdpi.com/1996-1073/16/5/2130/pdf?version=1677551028

Study on Optimization of Copper to Aluminum for Locomotive Finned Tube Radiator

Due to aerodynamic resistance, aerodynamic noise, and other problems, the further development of traditional high-speed electric multiple units (EMUs) on the open line has been seriously restricted, and the construction of a vacuum pipeline high-speed train system has become a new solution. In this paper, the Improved Detached Eddy Simulation (IDDES) is used to analyze the turbulent characteristics of the near wake region of EMU in vacuum pipes, so as to establish the important relationship between the turbulent boundary layer, wake, and aerodynamic drag energy consumption. The results show that there is a strong vortex in the wake near the tail, which is concentrated at the lower end of the nose near the ground and falls off from the tail. In the process of downstream propagation, it shows symmetrical distribution and develops laterally on both sides. The vortex structure far from the tail car is increasing gradually, but the strength of the vortex is decreasing gradually from the speed characterization. This study can provide guidance for the aerodynamic shape optimization design of the rear of the vacuum EMU train in the future and provide certain reference significance for improving the comfort of passengers and saving the energy consumption caused by the speed increase and length of the train.

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Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area of Vacuum Tube EMU

At present, China’s high-speed rail is in a period of rapid development. Most of the refrigerants used in Chinese motor train units at this stage are still R134a and R407c, which have an impact on the environment. In response to the environmental protection concept of green travel, it is of great significance to study the air conditioning system of motor train units using CO2 refrigerant. Using CFD simulation analysis technology, the heat transfer performance of the air side of the CO2 finned tube evaporator used in the air conditioning system of the motor train unit is studied. We select the air outlet temperature, pressure drop, heat transfer factor, and resistance factor as the objective function, in addition, monitoring points are set up in the air flow channel to monitor the turbulent flow field and pipe wall pressure. Our research shows that the cooling capacity of the CO2 evaporator can reach up to 29.76 kW, which can meet the heat exchange required in the air conditioning system of the motor train unit. In order to obtain a better structure and the conditions of the heat transfer effect, structural optimization was conducted. The simulation results demonstrate several trends: (i) With the augment of the air inlet velocity, the cooling capacity of the evaporator increases and the heat exchange effect improves; when the air inlet ve> 2.2 m/s, the effect of continuing to augment ve on heat exchange is weak. (ii) Following appropriate reduction of the diameter of the heat exchange tube, the wind resistance is reduced and the cooling capacity of the evaporator is improved. (iii) With the enlargement of the fin spacing, the turbulent motion in the flow channel can be fully developed, there is a peak in the change in the heat exchange tube area optimization factor, and the optimal fin spacing is between 1.6 mm and 1.7 mm; at this time, the average turbulent kinetic energy of the air side is larger and the turbulent dissipation rate is smaller. These results provide a reference for the practical application of CO2 refrigerant in the motor train unit.

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Ying Guan

Papers

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

Study on Optimization of Copper to Aluminum for Locomotive Finned Tube Radiator

Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area of Vacuum Tube EMU

CFD Simulation Study on the Air Side of a CO2 Evaporator in a Motor Train Unit Air Conditioning System