A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy.

The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.

Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis.

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.

High-Performance Lithium-Ion Storage of FeTiO3 with Morphology Adjustment and Niobium Doping.

Ferrous titanate (FeTiO3) has a high theoretical capacity and physical and chemical properties stability, so it is a potential lithium anode material. In this study, FeTiO3 nanopowder and nanosheets were prepared by the sol-gel method and the hydrothermal method. In addition, niobium-ion doping was carried out, the radius of Nb close to Ti so the Nb can easily enter into the FeTiO3 lattice. Nb can provide more free electrons to improve the electrochemical performance. Then, the effects of the morphology and niobium doping on the microstructure and electrochemical properties of FeTiO3 were systematically studied. The results show that FeTiO3 nanosheets have a better lithium storage performance than nanopowders because of its high specific surface area. A certain amount of niobium doping can improve the electrochemical performance of FeTiO3. Finally, a 1 mol% niobium-doping FeTiO3 nanosheets (1Nb-FTO-S) electrode provided a higher specific capacity of 782.1 mAh g-1 at 50 mA g-1. After 200 cycles, the specific capacity of the 1Nb-FTO-S electrode remained at 509.6 mAh g-1. It is revealed that an increased specific surface area and ion doping are effective means to change the performance of lithium, and the proposed method looks promising for the design of other inorganic oxide electrode materials.

Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM.

A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization algorithm (WGWOA) was proposed by combining whale optimization algorithm (WOA) and gray wolf optimization (GWO), and the rolling bearing signal was decomposed by using variational mode decomposition (VMD). Each eigenvalue was extracted as eigenvector after VMD, and the training and test sets of the fault diagnosis model were divided accordingly. The support vector machine (SVM) was used as the fault diagnosis model and optimized by using WGWOA. The validity of this method was verified by two cases of Case Western Reserve University bearing data set and laboratory test. The test results show that in the bearing data set of Case Western Reserve University, compared with the existing VMD-SVM method, the fault diagnosis accuracy rate of the WGWOA-VMD-SVM method in five repeated tests reaches 100.00%, which preliminarily verifies the feasibility of this algorithm. In the laboratory test case, the diagnostic effect of the proposed fault diagnosis method is compared with backpropagation neural network, SVM, VMD-SVM, WOA-VMD-SVM, GWO-VMD-SVM, and WGWOA-VMD-SVM. Test results show that the accuracy rate of WGWOA-VMD-SVM fault diagnosis is the highest, the accuracy rate of a single test reaches 100.00%, and the accuracy rate of five repeated tests reaches 99.75%, which is the highest compared with the above six methods. WGWOA plays a good optimization role in optimizing VMD and SVM. The signal decomposed by VMD is optimized by using the WGWOA algorithm without mode overlap. WGWOA has the better convergence performance than WOA and GWO, which further verifies its superiority among the compared methods. The research results can provide an effective improvement method for the existing rolling bearing fault diagnosis technology.

Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes.

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms. This paper focuses on the influence of big data analytics capability on the choice of cooperation mode, and the influence of their matching relationship on cooperation performance. Specifically, using game-theoretic models, we discuss two cooperation modes, data analytics is implemented individually (i.e., loose cooperation) by either firm, or jointly (tight cooperation) by both firms, and further discuss the addition of coordination contracts under the loose mode. Several important conclusions are obtained. Firstly, both firms' big data capability have positive effects on the selection of tight cooperation mode. Secondly, with the improvement of big data capability, the firms' innovative performance gaps between loose and tight mode will increase significantly. Finally, when the capability meet certain condition, the cost subsidy contract can alleviate the gap between the two cooperative models.

Preparation of epoxy/ZrO2 composite coating on the Q235 surface by electrostatic spraying and its corrosion resistance in 3.5% NaCl solution.

The epoxy coating containing ZrO2 nanoparticles modified with 3-aminopropyltriethoxysilane (APTES) was prepared by electrostatic spraying on the surface of Q235 mild steel. The effect of the concentration of APTES-modified ZrO2 nanoparticles on the corrosion resistance of epoxy coating was characterized and tested by FTIR spectroscopy, scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The results show that nano ZrO2 was successfully modified by a silane coupling agent. By adding an appropriate amount of APTES to modify nano ZrO2 in epoxy coating could significantly improve the corrosion resistance of the Q235 surface. When the mass fraction of nano ZrO2 is 2%, the composite coating shows the highest impedance value of about 1.0 × 105 Ω cm2 to achieve the best corrosion resistance.

Electrically Programmable Interfacial Adhesion for Ultrastrong Hydrogel Bonding.

Adjustable interfacial adhesion is of great significance in smart-hydrogel-related engineering fields. This study presents an electroadhesion strategy for universal and ultrastrong hydrogel bonding with electrically programmable strength. An ionic hydrogel containing lithium ions is designed to achieve hydrated-ion-diffusion-mediated interfacial adhesion, where external electric fields are employed to precisely control spatiotemporal dynamics of the ion diffusion across ionic adhesion region (IAR). The hydrogel can realize a universal, ultrastrong, efficient, tough, reversible, and environmentally tolerant electroadhesion to diverse hydrogels, whose peak adhesion strength and interfacial adhesion toughness are as high as 1.2 MPa and 3750 J m-2 , respectively. With a mechanoelectric coupling model, the dominant role of the hydrated ions in IAR played in the interfacial electroadhesion is further quantitatively revealed. The proposed strategy opens a door for developing high-performance adhesion hydrogels with electrically programmable functions, which are indispensable for various emerging fields like flexible electronics and soft robotics.

Numerical Simulation Based on a ZrO2-Coated Stainless-Steel Corrosion Experiment.

In this work, ZrO2-coated on the surface of 304 stainless steel was prepared by a sol-gel method to study the corrosion resistance. Based on the experimental results, an effective numerical model was established using a finite element method to simulate the electrochemical corrosion of ZrO2-coated stainless steel in a 5% NaCl solution. This model simulates the changes in electrode/electrolyte potential, ion concentration, and oxygen concentration during the polarization process and provides a relatively reasonable explanation for the influence of the density of ZrO2 on the corrosion resistance of stainless steel.

Effects of government regulations on Manufacturer's behaviors under carbon emission reduction.

This paper shifts the discussion of low-carbon technology from science to the economy, especially the reactions of a manufacturer to government regulations. One major concern in this paper is uncertainty about the effects of government regulation on the manufacturing industry. On the trust side, will manufacturers trust the government's commitment to strictly supervise carbon emission reduction? Will a manufacturer that is involved in traditional industry consciously follow a low-carbon policy? On the profit side, does equilibrium between a manufacturer and a government exist on deciding which strategy to undertake to meet a profit maximization objective under carbon emission reduction? To identify the best solutions to these problems, this paper estimates the economic benefits of manufacturers associated with policy regulations in a low-carbon technology market. The problem of an interest conflict between the government and the manufacturer is formalized as a game theoretic model, and a mixed strategy Nash equilibrium is derived and analyzed. The experiment results indicate that when the punishment levied on the manufacturer or the loss to the government is sizable, the manufacturer will be prone to developing innovative technology and the government will be unlikely to supervise the manufacturer.

Study on Sand Erosion and Tribological Behavior of TiO2 Films Prepared on a Glass Surface.

TiO2 films with one, three or five layers were prepared on a glass surface using the sol-gel method. The crystal structure, the surface morphology and the thickness of the films were characterized by X-ray diffraction, atomic force microscopy and ellipsometry. The tribological properties of the TiO2 films were investigated by a tribometer. TiO2 thin films were eroded by sand-air injection. The erosion behavior and mechanism of TiO2 thin films in a sandstorm were analyzed by scanning electron microscopy. The results showed that the films were highly abraded with increased erosion speed and dose of sand. With an increase in film layers, the erosion resistance and wear resistance of the TiO2 films increased gradually. The erosion mechanism consists of the film being damaged mainly from the cutting action of micro-scratches from low angle erosion. Alternatively, for high angle erosion, the material is damaged mainly by squeeze deformation by the action of erosion. Because of the high strength and toughness of the TiO2 thin films, the wear of its coating from high angle erosion is more severe than that from low erosion angle.

Corrosion and Tribological Behavior of ZrO2 Films Prepared on Stainless Steel Surface by the Sol-Gel Method.

ZrO2 films with one, two, and three layers were prepared on a 304 stainless steel surface through the sol-gel method, followed by sintering at 500, 600, and 700 °C. The crystal structure and the surface morphology of the films were characterized by X-ray diffraction and atomic force microscopy. The corrosion resistances of uncoated and coated specimens were studied by electrochemical corrosion tests in a 5% NaCl solution at room temperature. The tribological properties of ZrO2 films were investigated using a tribometer. The results showed that the crystal structure of ZrO2 partially transformed from the tetragonal phase to the monoclinic phase with a rise in sintering temperature. The grain size of the ZrO2 films grew, and the surface roughness of the films increased. However, with an increase in the number of film layers, the grain size and the surface roughness of the ZrO2 films decreased and the films became more uniform and denser. ZrO2 films effectively enhanced the corrosion and wear resistances of the stainless steel surface. With the increase of the sintering temperature and the number of layers in the film, the corrosion resistance of the ZrO2 films increased gradually, but the wear resistance of the films slowly decreased. The film with three layers, which was sintered at 700 °C, had the highest corrosion resistance. Nevertheless, the film with one layer, which was sintered at 500 °C, exhibited relatively well wear resistance.