Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

Investigation of the changes in physical properties of PES/PVC fabrics after aging

The aim of this work was to investigate the physical and mechanical performance of architectural polyester (PES)–poly(vinyl chloride) (PVC) membranes exposed to different artificial aging conditions. Two commercially available architectural membranes were chosen as research objects. The durability of the PES/PVC fabrics was evaluated by the loss in mechanical performance, scanning electron microscopy, and X-ray diffraction analysis in order to understand the effect of the degradation agents on the surface of the membranes. The mechanical performance of the PES/PVC membranes was unchanged. Scanning electron microscopy images of the tested materials showed initial cracks after aging. The X-ray fluorescence analysis showed that at the time of aging, the amount of Cl and Si decreased slightly, while Ti decreased by half, and Ca by volume increased twice. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47523.     

Statistical Piezotronic Effect in Nanocrystal Bulk by Anisotropic Geometry Control

Utilizing inner-crystal piezoelectric polarization charges to control carrier transport across a metal-semiconductor or semiconductor–semiconductor interface, piezotronic effect has great potential applications in smart micro/nano-electromechanical system (MEMS/NEMS), human-machine interfacing, and nanorobotics. However, current research on piezotronics has mainly focused on systems with only one or rather limited interfaces. Here, the statistical piezotronic effect is reported in ZnO bulk composited of nanoplatelets, of which the strain/stress-induced piezo-potential at the crystals’ interfaces can effectively gate the electrical transport of ZnO bulk. It is a statistical phenomenon of piezotronic modification of large numbers of interfaces, and the crystal orientation of inner ZnO nanoplatelets strongly influence the transport property of ZnO bulk. With optimum preferred orientation of ZnO nanoplatelets, the bulk exhibits an increased conductivity with decreasing stress at a high pressure range of 200–400 MPa, which has not been observed previously in bulk. A maximum sensitivity of 1.149 µS m⁻¹ MPa⁻¹ and a corresponding gauge factor of 467–589 have been achieved. As a statistical phenomenon of many piezotronic interfaces modulation, the proposed statistical piezotronic effect extends the connotation of piezotronics and promotes its practical applications in intelligent sensing.     

Preparation and electrochemical characterization of Ti-based amorphous metallic glass with high strength

Ti-based amorphous metallic glasses have excellent mechanical, physical, and chemical properties, which is an important development direction and research hotspot of metal composite reinforcement. As a stable, simple, efficient, and large-scale preparation technology of metallic powders, the gas atomization process provides an effective way of preparing amorphous metallic glasses. In this study, the controllable fabrication of a Ti-based amorphous powder, with high efficiency, has been realized by using gas atomization. The scanning electron microscope, energy-dispersive spectrometer, and X-ray diffraction are used to analyze surface morphology, element distribution, and phase structure, respectively. A microhardness tester is used to measure the mechanical property. An electrochemical workstation is used to characterize corrosion behavior. The results show that as-prepared microparticles are more uniform and exhibit good amorphous characteristics. The mechanical test shows that the hardness of amorphous powder is significantly increased as compared with that before preparation, which has the prospect of being an important part of engineering reinforced materials. Further electrochemical measurement shows that the corrosion resistance of the as-prepared sample is also significantly improved. This study has laid a solid foundation for expanding applications of Ti-based metallic glasses, especially in heavy-duty and corrosive domains.     

Characterization of barium titanate reinforced acrylonitrile butadiene rubber composites for flexible electronic applications: influences of barium titanate content

The aim of this study was to develop high dielectric constant flexible polymers with a highly efficient and cost‐effective approach using acrylonitrile butadiene rubber (NBR) as the polymer matrix and barium titanate (BT) as the high dielectric constant filler. The BT powder was synthesized with a solid‐state reaction and was characterized using a particle size analyzer, XRD, SEM and Fourier transform infrared spectroscopy. NBR/BT composites were fabricated using an internal mixer with various BT loadings up to 160 phr. The influence of BT loading on the cure characteristics and mechanical, dynamic mechanical, thermal, dielectric and morphological properties was determined. The incorporation of BT in the NBR matrix shortened scorch time and increased delta torque. The mechanical properties, thermal stability and dielectric constant were greatly improved and increased with BT loading. The results suggest that the reinforcement effect was achieved due to strong hydrogen bonding or polar–polar interactions between NBR matrix and BT filler. This is further corroborated by the good dispersion of BT filler in the NBR matrix observed with SEM imaging. These findings can be applied to produce high‐performance dielectric elastomers. © 2020 Society of Industrial Chemistry     

Hydraulic reactivity and cement formation of baghdadite

In this study, the hydraulic reactivity and cement formation of baghdadite (Ca₃ZrSi₂O₉) was investigated. The material was synthesized by sintering a mixture of CaCO₃, SiO₂, and ZrO₂ and then mechanically activated using a planetary mill. This leads to a decrease in particle and crystallite size and a partial amorphization of baghdadite as shown by X-ray powder diffraction (XRD) and laser diffraction measurements. Baghdadite cements were formed by the addition of water at a powder to liquid ratio of 2.0 g/ml. Maximum compressive strengths were found to be ~2 MPa after 3-day setting for a 24-h ground material. Inductively coupled plasma mass spectrometry (ICP-MS) measurements showed an incongruent dissolution profile of set cements with a preferred dissolution of calcium and only marginal release of zirconium ions. Cement formation occurs under alkaline conditions, whereas the unground raw powder leads to a pH of 11.9 during setting, while prolonged grinding increased pH values to approximately 12.3.     

Deacetylated Cellulose Acetate/Polyurethane Composite Nanofiber Membranes with Highly Efficient Oil/Water Separation and Excellent Mechanical Properties

Nowadays, oil pollution has become more serious, which causes great threats both to the ecological environment and human life. In this study, a novel type of multifunctional deacetylated cellulose acetate/polyurethane (d-M_CA:M_TPU) composite nanofiber membranes for oil/water separation are successfully fabricated by electrospinning, which show super-amphiphilicity in air, super-hydrophilicity in oil, and oleophobicity in water. All the d-M_CA:M_TPU composite nanofiber membranes with different mass ratios can be used as water-removing, oil-removing, and emulsion separation substance only by gravity driving force. The highest separation flux for water and oil reaches up to 37 000 and 74 000 L m⁻² h⁻¹, respectively, and all the separation efficiencies are more than 99%. They have outstanding comprehensive mechanics performance, which can be controlled by simply adjusting the mass ratios. They show excellent antifouling and self-cleaning ability, endowing powerful cyclic stability and reusability. Those results show that d-M_CA:M_TPU composite nanofiber membranes have great application prospects in oil/water separation.