基于SSA-VMD和2.5维谱的齿轮箱磨损故障诊断

针对行星齿轮箱实际工况中存在多种频率耦合无法直接提取故障特征频率的问题,提出一种基于樽海鞘群算法优化变分模态分解(SSA-VMD)结合2.5维谱的故障诊断方法。运用SSA优化VMD的参数;运用自相关系数对分解信号进行重构,降低噪声的干扰;最后运用2.5维谱对重构信号中的频率耦合进行解耦运算。搭建行星齿轮箱磨损故障全生命周期实验台采集振动信号,运用提出的方法解耦出参与耦合的故障频率成分,揭示了行星齿轮箱磨损故障演化规律。研究结果表明：随着磨损故障程度的加深,磨损故障特征频率明显增多。     

基于MRSVD与LMD的工业机器人交叉滚子轴承故障特征提取

针对奇异值分解(SVD)优化局部均值分解(LMD)方法提取工业机器人交叉滚子轴承振动信号微弱故障特征分量时出现的模态混淆现象,提出一种基于最大分辨率SVD与LMD的工业机器人交叉滚子轴承故障特征提取方法。以最大奇异值分辨率原则将一维振动信号构造成Hankel矩阵,采用SVD对Hankel矩阵进行分解得到奇异值序列;按照奇异值曲率谱原则及非目标值抑制原则对奇异值序列进行重构,将包含故障突变信息的重构奇异值序列进行SVD逆运算得到重构振动信号;最后利用LMD方法对重构振动信号进行故障特征提取,得到能够表征原始振动信号振动特征的故障特征分量。通过与SVD优化LMD方法对比可知,该方法完整地提取了工业机器人交叉滚子轴承振动信号的微弱故障特征分量,改善了模态混淆现象。     

Using Singular Value Decomposition to Characterize Protein–Protein Interactions by In‐cell NMR Spectroscopy

Distinct differences between how model proteins interact in‐cell and in vitro suggest that the cytosol might have a profound effect in modulating protein–protein and/or protein–ligand interactions that are not observed in vitro. Analyses of in‐cell NMR spectra of target proteins interacting with physiological partners are further complicated by low signal‐to‐noise ratios, and the long overexpression times used in protein–protein interaction studies may lead to changes in the in‐cell spectra over the course of the experiment. To unambiguously resolve the principal binding mode between two interacting species against the dynamic cellular background, we analyzed in‐cell spectral data of a target protein over the time course of overexpression of its interacting partner by using single‐value decomposition (SVD). SVD differentiates between concentration‐dependent and concentration‐independent events and identifies the principal binding mode between the two species. The analysis implicates a set of amino acids involved in the specific interaction that differs from previous NMR analyses but is in good agreement with crystallographic data.     

彩涂胶辊用聚氨酯弹性体的研制

合成了一种彩涂胶辊用聚氨酯弹性体材料,考察了聚酯多元醇、异氰酸酯、扩链剂、催化剂、操作工艺等因素对材料性能的影响。所制聚氨酯弹性体具有较高的力学性能、良好的耐溶剂性能、较好的切削加工性和较长的使用寿命。     

推进剂药粒干燥过程的安全性分析

分析了推进剂药粒在干燥过程中发生热分解、燃烧或热爆炸的起因。通过比较几种典型干燥方法,了解其干燥过程的优缺点。根据推进剂干燥过程中所发生的物理化学变化,讨论了干燥过程中硝化甘油蒸发及推进剂热分解、燃烧或热爆炸的机理。通过烘箱法模拟热分解,找到了发生热分解、燃烧或热爆炸的时间和温度点。最后分析了推进剂药粒在干燥过程中的危险性。     

Continuous monitoring of the dynamic behavior of a high‐rise telecommunications tower

This article describes the experimental evaluation of the dynamic effects induced by wind on a high‐rise telecommunications tower based on a permanent monitoring system. Monte da Virgem telecommunications tower is located near the city of Porto (Portugal), and its structure consists in a reinforced concrete shaft and a steel mast, with a total height of 177 m. The monitoring system includes accelerometers, anemometers, and a meteorological station, allowing the characterization of the maximum accelerations of the structure and wind regimes during a period of 6 months. The analysis of the results enabled identifying specific events, denominated as critical events, for which the dynamic response of the tower under wind actions appears significantly amplified due to wind aeroelastic instability phenomena in the steel mast. The automatic identification of the critical events was based on the application to the acceleration's records of an autoregressive model and estimation of its optimal order number based on a singular value decomposition. The results proved the robustness and efficiency of the proposed technique in identifying the number, duration, and maximum amplitude of accelerations associated to the critical events, envisaging its potential integration in structural health monitoring systems.     

基于变分模态分解与支持向量数据描述结合的液压泵性能退化评估方法

针对液压泵性能退化过程定量评估,提出了变分模态分解(VMD)和支持向量数据描述(SVDD)相结合的综合评估方法。利用VMD方法将信号分解成一系列不同频率成分的BIMF分量,并用SVDD方法对异常点进行剔除;使用SVDD方法对正常状态样本进行训练得到超球体模型,并计算各样本到球心的距离;再将各样本到球心的距离转化为隶属度,作为性能退化指标。通过对轴向柱塞泵滑靴磨损和松靴故障实验数据分析,验证了该性能退化评估方法的有效性。     

基于改进LMD的齿轮故障特征增强方法

针对齿轮发生故障时信号的调制特点,采用相关分析对局部均值分解(Local Mean Decomposition,LMD)解调方法进行改进,并提出了基于改进LMD的故障特征增强方法。将该方法应用于实际齿轮断齿故障特征的提取,结果表明:该方法能有效地增强齿轮故障特征,其增强效果优于传统的Hilbert包络解调分析方法。     

EMD-based random decrement technique for modal parameter identification of an existing railway bridge

Vibrational measurement data are often nonstationary and modal parameter identification based on these data is of practical value for structural health monitoring and condition assessment. The empirical mode decomposition (EMD) is a most recent tool for analysis of nonstationary signals. An EMD-based random decrement (RD) technique is presented to identify modal parameters from monitoring vibrational data. The nonstationary measurement data are first decomposed into a series of quasi-stationary intrinsic mode functions (IMFs) by EMD. The RD technique is then applied to the selected IMFs to obtain the free-decay response. The modal frequencies and damping ratios are finally identified from the free-decay response by minimizing the error between the measured free-decay responses and the predicted responses from a parametric model. The present method is applied to extract the modal parameters of the Nanjing Yangtze River Bridge from the measured responses. The identification result is compared to those from finite element analysis as well as from the experimental result identified with the peak-picking (PP) method. In addition, the modal frequencies of the bridge loaded with heavy trains are also identified and compared to the ‘empty’ bridge. The EMD-based random decrement (RD) technique provides an effective and promising tool for modal parameter identification for large bridges and other structures.     

AC在PVC—U体系中分解规律研究

研究了AC发泡剂的分解规律，在PVC－U体系中添加剂对其分解规律的影响，以及AC分解对PVC热稳定性的影响，为低泡PVC－U的加工提供了理论依据。