Research on fault law of rolling bearing under different fault levels and loads with HHT method

Bearing is one of the most important components of rotating machinery. The vibration signals are generally nonlinear and nonstationary while operating. The failed rolling bearing will damage to the machine, or cause a serious loss of property. There are a lot of methods about fault diagnosis of bearing, such as shock pulse method, resonance demodulation. Especially the HHT (Hilbert-Huang Transform) method with the adaptive advantage has gradually become a very promising method to extract the characteristics of nonlinear, nonstationary signal. In this paper the variant energy method was introduced in HHT to reduce the computation of the decomposed signal, which effectively improved the computation, and then an experimental platform was designed and established. The bearing fault categories can be diagnosed correctly in dealing with the vibration signals using this method and the fault law is discovered that the trend of the vibration signal fault characteristic frequency amplitude changes with the load increasing. The bearing failure mechanism provides beneficial reference for further research of nonlinear signal analysis

Bose-Einstein Condensation of Long-Lifetime Polaritons in Thermal Equilibrium

Exciton-polaritons in semiconductor microcavities have been used to demonstrate quantum effects such as Bose-Einstein condensation, superfluity, and quantized vortices. However, in these experiments, the polaritons have not reached thermal equilibrium when they undergo the transition to a coherent state. This has prevented the verification of one of the canonical predictions for condensation, namely the phase diagram. In this work, we have created a polariton gas in a semiconductor microcavity in which the quasiparticles have a lifetime much longer than their thermalization time. This allows them to reach thermal equilibrium in a laser-generated confining trap. Their energy distributions are well fit by equilibrium Bose-Einstein distributions over a broad range of densities and temperatures from very low densities all the way up to the threshold for Bose-Einstein condensation. The good fits of the Bose-Einstein distribution over a broad range of density and temperature imply that the particles obey the predicted power law for the phase boundary of Bose-Einstein condensation

Bifurcation and singularity analysis of HSLDS vibration isolation system with elastic base

In order to reveal the bifurcation mechanism and optimize the system design for high-static-low-dynamic-stiffness (HSLDS) vibration isolation system (VIS) with elastic base, the local bifurcation analyses both in unfolding parameter space and physical parameter space were carried out theoretically and numerically. Firstly, the restoring force of the HSLDS-VIS was approximated to linear and cubic stiffness by applying the Maclaurin series expansion and the motion equations of HSLDS-VIS with elastic base were established. Subsequently, the motion equations of HSLDS-VIS with elastic base were formulated to transform the system into a standard form and the averaging method was applied to obtain the single-variable bifurcation equation for the HSLDS-VIS with elastic base in case of primary resonance and 1:2 internal resonance. Furthermore, the transition sets and bifurcation diagrams in the unfolding parameter space were studied by means of singularity theory. Finally, for the engineering application, the transition sets were transferred back to the physical parameter space, thus to obtain the bifurcation diagrams of the amplitude with respect to the external force. The numerical simulation results show that the local bifurcations of HSLDS-VIS with elastic base in case of 1:2 internal resonance are considerable complex and need to be analyzed in six two-parameters spaces, meanwhile, the necessary condition of multiple solutions lies in some physical parameters, which can provide a theoretical basis and reference for design and application of the HSLDS-VIS with elastic base

Biogeographical Variation and Population Genetic Structure of Sporisorium scitamineum

A total of 100 Sporisorium scitamineum isolates were investigated by inter simple sequence repeat (ISSR) and single primer-sequence related amplified polymorphism (SP-SRAP) markers. These isolates were clearly assorted into three distinct clusters regardless of method used: either cluster analysis or by principal component analysis (PCA) of the ISSR, SP-SRAP, or ISSR + SP-SRAP data set. The total gene diversity (Ht) and gene diversity between subpopulations (Hs) were estimated to be 0.34 to 0.38 and 0.22 to 0.29, respectively, by analyzing separately the ISSR and SP-SRAP data sets, and to be 0.26–0.36 by analyzing ISSR + SP-SRAP data set. The gene diversity attributable to differentiation among populations (Gst) was estimated to be 0.35 and 0.22, and the gene flow (Nm) was 0.94 and 1.78, respectively, when analyzing separately ISSR and SP-SRAP data set, and was 0.27 and 1.33, respectively, when analyzing ISSR + SP-SRAP data set. Our study showed that there is considerable genetic variation in the analyzed 100 isolates, and the environmental heterogeneity has played an important role for this observed high degree of variation. The genetic differentiation of sugarcane smut fungus depends to a large extent on the heterogeneity of their habitats and is the result of long-term adaptations of pathogens to their ecological environments

Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor

Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units

New Nonlinear Cumulative Fatigue Damage Model Based on Ecological Quality Dissipation of Materials

The failure of many aircraft structures and materials is caused by the accumulation of fatigue damage under variable-amplitude cyclic loading wherein the damage evolution of materials is complicated. Therefore, to study the cumulative fatigue damage of materials under variable-amplitude cyclic loading, a new nonlinear fatigue damage accumulation model is proposed based on the ecological quality dissipation of materials by considering the effects of load interaction and sequence. The proposed new model is validated by the test data obtained for three kinds of material under multilevel fatigue loading. Compared with the Miner model and Kwofie model, the proposed model can more effectively analyse the accumulative damage and predict fatigue life of different materials under variable-amplitude cyclic loading than others. The study provides a basis for predicting fatigue life accurately and determining reasonable maintenance periods of aircraft structures

Improved design of transient power sharing strategy for AES multi-gensets in response to high power load mutation

The Medium Voltage Direct Current (MVDC) Integrated Power System (IPS) represents the developing trend of marine ship power. It facilitates the provision of high quality energy for weaponry, however, high power load switching will have a severe impact on power system as well as on gas turbine gensets, which is a critical problem needs to be solved urgently. In this paper, based on the traditional IPS hierarchical control strategy, an improved design method of transient power sharing is proposed for multi-gensets operation. Firstly, by analyzing the transient fluctuation process of voltage and speed, the law of system energy coupling transfer is illustrated and a strategy of equalizing torque impact is proposed. Then, a set of speed compensation factors is introduced to achieve the equalization of transient speed regulation (TSR). Finally, the effectiveness of the proposed improved control strategy is verified by MATLAB/Simulink simulation. Compared to before the improvement, the gensets operate away from the limiting boundary of bearing torque impact and speed deviation, and the bus voltage stability is also enhanced