Search

Abnormal tool wear is a significant problem encountered in machining. MoS2 has the function of friction reduction; thus, to mitigate friction, two Ni-based coatings, Ni60 and Ni60 + 15 wt.% Ni/MoS2, were obtained on tungsten steel using supersonic plasma technology. The microstructure, phase structure, microhardness, fatigue properties, and friction and wear properties of the two coatings were characterized. The results show that the two plasma-sprayed Ni-based coatings have desirable structures. The addition of Ni-coated MoS2 can effectively improve the hardness of the coatings, with values reaching as high as 735 HV. The speed of rotation of the friction ball was set as 200 r/min, and the cutting force was 201.6 N. The fatigue performance as well as the friction and wear performance of the coating are simulated using ANSYS. The fatigue performance is improved by 12.6 % after adding 15 wt.% Ni/MoS2, and the friction system of the coating becomes stable after 25 min of contact. The addition of 15 wt.% Ni/MoS2 can effectively improve the performance of the tool material.

The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.

The present study envisaged the preparation of four kinds of coatings on 45# steel substrate by using the Supersonic plasma spraying (SPS) technology. The structure and morphology of the four coatings were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM), and the hardness, wear resistance and corrosion resistance of the coatings were studied. The results show that there was an increase in the ceramic particles. The wear resistance and corrosion performance of the coatings was improved to some extent. The hardness of N6WA decreased with an increase in the coating distance, but it showed good wear resistance. The wear mechanism was found to involve mainly the abrasive wear. A series of electrochemical tests were carried out in 3.5 wt.% NaCl solution, and the corrosion behavior and corrosion resistance mechanism were studied.

Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models’ predictive performance and application status.

Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.

The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

The helical planetary transmission system (HPTS) is widely used in wind power, hydropower, and shield machine, which operating conditions are extremely complicated, and the existence of component installation errors will result in response deviations to the theoretical design. A dynamic model for slice type bending-torsional-pendulum of the HPTS is established in the lumped parameter method considering the installation error of shaft (IEOS) for the 1st planet gear, helical meshing characteristics, and bearing support characteristics. The vibration response characteristics of the HPTS under the IEOS for the 1st planet gear are studied. The results have shown that the FFT amplitudes for the horizontal vibration acceleration of the sun gear increased by 8.3%, and the axial vibration acceleration of the ring gear increased by 11.5%, respectively. The maximum magnitude increase of FFT amplitude for the vibration acceleration under a positive 0.13 mm IEOS for the 1st planet gear is the axial direction of the ring gear. The results indicate that the IEOS needs to be thoroughly introduced into the HPTS to simulate the actual operating conditions of the system.

Driver identification is very important to realizing customized service for drivers and road traffic safety for electric vehicles and has become a research hotspot in the field of modern automobile development and intelligent transportation. This paper presents a comprehensive review of driver identification methods. The basic process of driver identification task is proposed as four steps, the advantages and disadvantages of different data sources for driver identification are analyzed, driver identification models are divided into three categories, and the characteristics and research progress of driver identification models are summarized, which can provide a reference for further research on driver identification. It is concluded that on-board sensor data in the natural driving state is objective and accurate and could be the main data source for driver identification. Emerging technologies such as big data, artificial intelligence, and the internet of things have contributed to building a deep learning hybrid model with high accuracy and robustness and representing an important gradual development trend of driver identification methods. Developing a driver identification method with high accuracy, real-time performance, and robustness is an important development goal in the future.

Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.

Battery state of charge (SOC), state of health (SOH), and state of power (SOP) are decisive factors that influence the energy-management system (EMS) performance of electric vehicles. However, the accurate estimation of SOC, SOH, and SOP remains a challenge due to the high nonlinearity of the battery dynamic characteristics and the strong coupling among the states. In this paper, different methods of single-state and two-state joint estimation are classified and discussed, including SOC/SOH and SOC/SOP joint estimation methods, and their advantages and limitations are analyzed. On this basis, key issues of joint multi-state estimation are discussed, and suggestions for future work are made.

Damping performance of the plates with constrained layer damping (CLD) treatment mainly depends on the layout of CLD material and the material physical properties of the viscoelastic damping layer. This paper develops a concurrent topology optimization methodology for maximizing the modal loss factor (MLF) of plates with CLD treatment. At the macro scale, the damping layer is composed of 3D periodic unit cells (PUC) of cellular viscoelastic damping materials. At the micro scale, due to the deformation of viscoelastic damping material affected by the base and constrained layers, the representative volume element (RVE) considering a rigid skin effect is used to improve the accuracy of the effective constitutive matrix of the viscoelastic damping material. Maximizing the MLFs of CLD plates is employed as the design objectives in optimization procedure. The sensitivities with respect to macrodesign variables are formulated using the adjoint vector method while considering the contribution of eigenvectors, while the influence of macroeigenvectors is ignored to improve the computational efficiency in the mesosensitivity analysis. The macro and meso scales design variables are simultaneously updated using the Method of Moving Asymptotes (MMA) to find concurrently optimal configurations of constrained and viscoelastic damping layers at the macro scale and viscoelastic damping materials at the micro scale. Two rectangular plates with different boundary conditions are presented to validate the optimization procedure and demonstrate the effectiveness of the proposed concurrent topology optimization approach. The effects of optimization objectives and volume fractions on the design results are investigated. The results indicate that the optimized layouts of the macrostructure are dependent on the objective mode and the volume fraction on the meso scale. The optimized designs on the meso scale are mainly related to the objective mode. By varying the volume fraction on the macro scale, the optimized designs on the meso scale are different only in their detailed size, which is reflected in the values of the equivalent constitutive matrices.

As the most important element in improving the service life of a mechanical system, surface coating materials are of key importance in the development of high-end equipment. To improve the comprehensive properties of the surface coating, the common core-shell structure/Ni60 coating was prepared on the surface of #45 steel by supersonic plasma spraying. The results show that the Ni60 has no layer structure, pores, cracks, and other defects on the surface of the coating. The nanohardness of the NNA(Ni60/Ni (Al) mixture powder), NNM(Ni60/Ni (MoS2) mixture powder), and NNC(Ni60/Ni (C) mixture powder) coatings is 6815 μN, 5750 μN, and 2000 μN. The results show that the wear resistance of the NNA coating is better. In the friction and wear test, the NNA coating mainly shows adhesive wear and abrasive wear, while the NNM and NNC coatings mainly show abrasive wear.

This paper presents microstructural topology optimization of viscoelastic materials for the plates with constrained layer damping (CLD) treatments. The design objective is to maximize modal loss factor of macrostructures, which is obtained by using the Modal Strain Energy (MSE) method. The microstructure of the viscoelastic damping layer is composed of 3D periodic unit cells. The effective elastic properties of the unit cell are obtained through the strain energy-based method. The density-based topology optimization is adopted to find optimal microstructures of viscoelastic materials. The design sensitivities of modal loss factor with respect to the design variables are analyzed and the design variables are updated by Method of Moving Asymptotes (MMA). Numerical examples are given to demonstrate the validity of the proposed optimization method. The effectiveness of the optimal design method is illustrated by comparing a solid and an optimized cellular viscoelastic material as applied to the plates with CLD treatments.

Porous titanium was characterized after its surface modification by acid and alkali solution immersion. The results show that the acid surface treatment caused the emergence of flocculent sodium titanate and induced apatite formation. The surface modification of porous titanium promotes biological activation, and the application of porous titanium is also improved as an implant material because of the existence of C and Si. Keywords: Porous titanium, Surface modification, Microstructure, XPS

Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

The traditional acoustic-based diagnosis (ABD) technique based on single-channel testing has a significant engineering value. Since its diagnosis robustness is sensitive to sound signal acquisition location, it develops slowly. To solve this problem, the 2-dimensional (2D) sound field variation near the machine is adopted for diagnosis by the near-field acoustic holography (NAH)- based fault diagnosis method with array measurement. However, its performance is limited due to the neglect of the sound field normal change information. To dig the sound field fault information further, a 2.5-dimensional (2.5D) acoustic field diagnosis method is presented in this paper and its performance compared with the 2D technology is verified by the bearing diagnostic test. Different from the 2D technique with only one source image, the 2.5D acoustic field model consists of source image, holographic sound image, and the differences between them, and its effective feature model is constructed by Gabor wavelet feature extraction and random forest feature reduction algorithm. The diagnostic effect of the 2.5D technique compared with the 2D technique increases more than 11% in the bearing diagnostic test. It provides new ideas for the development of the NAH-based fault diagnosis method, and further improves the ABD technique-based array measurement.

This paper deals with an optimal layout design of the constrained layer damping (CLD) treatment of vibrating structures subjected to stationary random excitation. The root mean square (RMS) of random response is defined as the objective function as it can be used to represent the vibration level in practice. To circumvent the computationally expensive sensitivity analysis, an efficient optimization procedure integrating the pseudoexcitation method (PEM) and the double complex modal superposition method is introduced into the dynamic topology optimization. The optimal layout of CLD treatment is obtained by using the method of moving asymptote (MMA). Numerical examples are given to demonstrate the validity of the proposed optimization procedure. The results show that the optimized CLD layouts can effectively reduce the vibration response of the structures subjected to stationary random excitation.

Mechanical fault diagnosis can prevent the deterioration of mechanical equipment failures and is important for the stable operation of mechanical equipment. Firstly, this paper reviews three basic methods of fault diagnosis and common methods of data-driven fault diagnosis, focusing on the characteristics and advantages of deep learning and convolutional neural networks. Then, the basic structure and working principle of CNN (Convolutional Neural Networks) and some basic methods to achieve better training results are introduced. In the next place, from data processing, data fusion, sample set construction, and so on, it is reviewed that the method of fault diagnosis based on CNN and their application scenarios and advantages and disadvantages; for another, the related knowledge and concepts of transfer learning are introduced, and some current application scenarios and advantages and disadvantages of mechanical fault diagnosis techniques combining migration learning and convolutional neural networks are reviewed. Finally, the current difficulties and challenges of convolutional neural networks are discussed, and the research directions have been prospected for CNN applied to the field of fault diagnosis. Although there is quite some similar literature reviewed, this review aims to introduce the basic methods of fault diagnosis, which draw forth the basic applications of the fault diagnosis of data-driven, CNN in the domain of fault diagnosis, and the application scenarios and advantages and disadvantages of combining TL (Transfer Learning) and CNN in fault diagnosis, as well as some problems and prospects. It helps researchers to have a basic understanding of this. [ABSTRACT FROM AUTHOR]

A subdivision transformation method evaluated for weakly singular integrals is proposed in this paper, and the method is implemented as follows: based on the position of the source points, the shape information of the elements and the nearest distance from the source point to the integral element, a subdivision technology is constructed at first. With this subdivision technology, the integral element can be divided into several integral blocks with good shapes. And then, a simpler coordinate transformation method is constructed to remove the weak singularities of the integral blocks obtained by the subdivision technology. Compared with the conventional polar coordinate transformation method, the present transformation method does not need to calculate their integral interval, which is simpler and more effective to implement. Finally, the paper gives three numerical examples to verify the accuracy and validity of the present method. [ABSTRACT FROM AUTHOR]

Abnormal tool wear is a significant problem encountered in machining. MoS2 has the function of friction reduction; thus, to mitigate friction, two Ni-based coatings, Ni60 and Ni60 + 15 wt.% Ni/MoS2, were obtained on tungsten steel using supersonic plasma technology. The microstructure, phase structure, microhardness, fatigue properties, and friction and wear properties of the two coatings were characterized. The results show that the two plasma-sprayed Ni-based coatings have desirable structures. The addition of Ni-coated MoS2 can effectively improve the hardness of the coatings, with values reaching as high as 735 HV. The speed of rotation of the friction ball was set as 200 r/min, and the cutting force was 201.6 N. The fatigue performance as well as the friction and wear performance of the coating are simulated using ANSYS. The fatigue performance is improved by 12.6 % after adding 15 wt.% Ni/MoS2, and the friction system of the coating becomes stable after 25 min of contact. The addition of 15 wt.% Ni/MoS2 can effectively improve the performance of the tool material. [ABSTRACT FROM AUTHOR]

In this work, a revised Bingham model for magneto-rheological damper is used to investigate the primary resonance reduction in the double-layer semi-active isolation system of marine auxiliary machinery. An analytical solution for the auxiliary double-layer semi-active isolation system's primary resonance is obtained with an averaging method, and this is verified numerically using the Maple software. The effect of model parameters of magneto-rheological damper on the system's vibration transmissibility is studied. The research results show that the damping of magneto-rheological damper and the control force have a significant effect on the vibration transmissibility in the resonance region. The vibration transmissibility of the double-layer semi-active isolation system decreases with increase in damping of the magnetorheological damper and control force. Yet the zero force velocity contributes very little to changes in vibration transmissibility. [ABSTRACT FROM AUTHOR]