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Gear fault detection based on vibration analysis has been proven to be effective in fault identification. However， the extraction and identification of vibration signals characterizing early wear have not been solved well. This paper proposes a method for early wear diagnosis of gears， combining variational mode decomposition (VMD) based on spectrum correlation analysis with kernel support vector machine (SVM). For weak gear vibration signals that can characterize early wear， the modal numbers are initialized by an approximate complete reconstruction criterion. Meanwhile， the frequency corresponding to the maximum value of the signal power spectral density is used to initialize the center frequency of the VMD method. It is used to effectively extract gear wear information from gear vibration signals and then be combined with kernel support vector machines for early wear diagnosis of gears. The experimental results show that the proposed method can effectively overcome the problem that the modal numbers cannot be preset with large background noise， be with better robustness to noisy situations， and achieve a diagnostic accuracy of 94.4%， which provides a solution for early wear detection of gears. [ABSTRACT FROM AUTHOR]

Aiming at the urgent demands of intelligent fault diagnosis for the key components of helicopter main reducers， the dynamic response characteristics of the system under the typical fault state are analyzed from the perspective of dynamic modeling， which is conducive to deepening the understanding of fault mechanism and advanced diagnosis algorithm. The fault dynamic models considering multi-nonlinear factors are developed for the typical components of a helicopter main reducer using the lumped-parameter method， and the multi-body software is applied for simulation modeling and verification. The results show that the modulation phenomenon between meshing frequency and local fault characteristic frequency is clear and the circumferential responses of the planet gear are obviously greater than the translational responses. these models can effectively reveal the vibration characteristics under typical faults and provide guidance for fault diagnosis of the helicopter transmission system. [ABSTRACT FROM AUTHOR]

Many existing arrhythmia researches focus on the separation of different frequency characteristic components in the ECG signal. However, the contribution of different subsequences to the final target decision-making is lack of research and analysis. In order to enhance the impact of high-contribution subsequences on the classifier, a recognition method combining variable weight singular spectrum analysis and deep learning is proposed. Multiple subsequences are obtained through singular spectrum analysis. The Gini coefficient under the random forest is calculated by the singular value of each sequence and used as the weight. The sequence samples with variable weights are used to train the neural network model, which can mine useful information more efficiently and further improve the recognition accuracy. The accuracy rate of final arrhythmia recognition is 98. 35%, and Macro-Fl is 97. 95%. Compared with the traditional fixed weight, the proposed recognition method of variable weight has a significant improvement in various performance indicators. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to extract the fault features of the main shaft bearing of turboshaft engines， and that the background noise has a large interference， an improved second-order cyclostationary blind deconvolution (CYCBD) method is proposed to extract the fault feature frequency under strong noise background. In this method， particle swarm optimization (PSO) is used to optimize the filter length parameters for the CYCBD method. Firstly， the signal model is established based on the vibration characteristics of the fault bearing， and then the envelope spectrum fault feature ratio (FFR) is maximized by PSO algorithm. The optimal filter length parameter is input into the CYCBD method， and the envelope spectrum of the filtered signal is analyzed to extract the fault feature frequency. Finally， the proposed method is applied to the measured signal， and the efficiency and accuracy of fault feature extraction are improved compared with the traditional envelope spectrum analysis， which verifies the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

A detailed study on a high-iron red mud from Shandong province in the perspective of process mineralogy were conducted through chemical analysis, XRD, optical microscope, laser particle size analysis, energy spectrum analysis and BPMA automatic analyzer. The results show that the red mud has a fine particle size, and the chemical composition and mineral ( phase) composition are complex. The main minerals ( phases) in red mud are particulate silicon slag, hematite, iron-aluminum oxide, limonite, as well as trace amounts of sodium aluminosilicate, iron-titanium oxide, quaitz, gibbsite, bauxite, feldspar, and mica, etc. Iron occurs in the form of hematite, limonite, iron-aluminum oxide and particulate silicon slag. There is a large amount of particulate silicon slag in red mud, but the mineral ( phase) particle size is fine. The key to taking full advantage of the red mud is how to make use of iron, aluminum, titanium and sodium in the particulate silicon slag. [ABSTRACT FROM AUTHOR]

The U-typed reduction chamber is the core reaction device of the iron suspension roaster. In order to study the fluidization characteristics, a cold experiment system of U-typed reduction chamber was established. Based on the experimental data collected by pressure fluctuation method, the following conclusions were drawn. Aeration velocity and particle size were the main factors affecting the initial fluidization velocity, while the initial bed height had little effect. With the increasing of initial bed height and aeration velocity, the pressure fluctuation in fluidization chamber became lower, but opposite result was obtained for particle size. Besides, power spectrum analysis revealed that the formation, coalescence and fragmentation of bubbles were the fundamental reasons for pressure fluctuation. The results would provide theoretical support for the design and optimization of the operating parameters during suspension magnetization roasting experiments. [ABSTRACT FROM AUTHOR]

Nano cuprous oxide(Cu2O)powders were prepared by plasma induced cathode discharge electrolysis in CuSO4 solution， and the components and morphology of the prepared particles were investigated by various concentrations， electrolysis voltage and time. The results showed that a higher concentration of solution had a positive effect on the formation of Cu2O phase. As the discharge time extended， the particles aggregated and the size increased from 100nm to 400nm. Increasing the voltage was beneficial for the refinement of particles from 200nm to 40nm.Emission spectrum analysis indicated that there existed hydroxyl radicals and Cu atoms in plasma emission area， which was suggested that the formation mechanism of Cu2O. Theoretical calculation showed that excitation temperature， the electric field intensity and the arc column jet velocity was 9563K， 2.4×105V/m， and 73m/s， respectivively. [ABSTRACT FROM AUTHOR]

Flotation tests， Zeta potential measurements， infrared spectrum analyses， EDLVO calculations and SEM analyses were used to investigate the effect of citric acid on entrainment behavior of fine chlorite in hematite reverse flotation. Flotation results illustrated that citric acid promotes entrainment behavior of fine chlorite， and is conducive to removing chlorite in concentrate. Zeta-potential tests and infrared spectrum analyses indicated that adsorption of citric acid on the surfaces of chlorite and hematite increases their electronegativity and enhances the electrostatic repulsion between hematite and chlorite particles. EDLVO calculations demonstrated that the attraction between hematite and fine chlorite is transformed into repulsion after interacting with citric acid， and the calculation results are consistent with those of SEM. [ABSTRACT FROM AUTHOR]