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.     

Viscoplastic behavior of AA7075 aluminum alloy at high strain rate

Journal of Mechanical Science and Technology - The flow stress increases with the increase in strain rate. This phenomenon is the strain rate effect of plastic deformation. Hopkinson experiment...     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

Crystallization and spectroscopic properties in Er3+ doped oxyfluorogermanate glass ceramics containing Na

The Er³⁺ doped oxyfluorogermanate glasses, with a composition containing Na element, were synthesized by the conventional melting–quenching technique. When Na element was introduced into the composition of oxyfluorogermanate glass, the crystals behavior was investigated in details. Depending on the annealing procedure supplied, thermal annealing of precursor glasses in the system GeO₂/BaF₂/AlF₃/Na₂O/NaF/ZnO/GdF₃/ErF₃ led to the precipitation of different crystal phase nanocrystals. It was confirmed the nanocrystals in GC600 is orthorhombic NaBaAlF₆ which led to enhance obviously in the UC luminescence of Er³⁺. However, the nanocrystals in G585 led to decrease in the UC luminescence, which indicated few Er ions enter into the lattice of this nanocrystal phase. The reason of the decrease in UC emission intensity of GC585 was analyzed.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

The dielectric,thermal properties and crystallization mechanism of Li–Al – B–Si – O glass – Ceramic systems as a new ULTCC material

Li–Al–B–Si–O (LABS) glass-ceramics with a sintering temperature of 600 °C were studied for ultra-low temperature co-fired ceramics (ULTCC) applications. The crystal phase of LABS glass-ceramics is dendritic β-spodumene. The permittivity and dielectric loss of LABS glass-ceramics are ε_r = 5.8 and tgδ = 1.3 × 10⁻³ at 10 MHz, respectively. The coefficient of thermal expansion (CTE) of LABS glass-ceramics is 3.23 ppm/°C, which is close to that of silicon. The dielectric and thermal properties of LABS glass-ceramics are closely correlated to the degree of its crystallization. The permittivity decreases continually while the dielectric loss decreases first and slightly increases with the increasing of crystallization of β-spodumene. The CTE of LABS glass-ceramics decreases as β-spodumene crystallized from LABS glass. The crystallization kinetic and mechanism of LABS glass-ceramics indicate that the β-spodumene crystallizes in a two-dimensional interfacial growth mechanism due to the migration of Li-ions. The diffusion coefficients derived from energy-dispersive X-ray spectroscopy (EDS) results indicated that both Al and Ag electrodes have good compatibilities with ULTCC tapes, which could reduce the cost of multilayer electro-ceramic devices dramatically by using the ULTCC and base metallization.     

Evolution mechanism of the microstructure and mechanical properties of plasma-sprayed yttria-stabilized hafnia thermal barrier coating at 1400 °C

Yttria stabilized hafnia (Hf_0.84Y_0.16O_1.92, YSH16) coatings were sprayed by atmospheric plasma spraying (APS). The effects of thermal aging at 1400 °C on the microstructures, mechanical properties and thermal conductivity of the coatings were studied. The results show that the as-sprayed coating was composed of the cubic phase, and the nano-sized monoclinic (M) phase was precipitated in the annealed coating. The presence of M phase effectively constrained the sintering of the coating due to its superior sintering-resistance. The Young's modulus kept at a nearly same level of ~78 GPa even after annealing, and the coating annealed for 6 h yielded a maximum value of hardness but revealed a declining tendency in the Vicker's hardness with prolonged sintering time. The thermal conductivity increased from 0.8-0.95 W m^-1 K^-1 at as-sprayed state to 1.6 W m^-1 K^-1 after annealing at 1400 °C for 96 h. The dual-phase coating is promising to serve at temperatures above 1400 °C due to its excellent thermal stability and mechanical properties.     

Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.