基于EMD和Gabor变换的发动机曲轴轴承故障特征提取

针对发动机振动信号的非平稳特点,提出了一种基于经验模态分解(EMD)和Gabor变换相结合的曲轴轴承故障特征提取新方法。通过EMD方法将发动机非稳态加速振动信号分解成多个本征模态函数(IMF),对与原信号相关性强的前4阶IMF分量进行Gabor变换,从各阶分量Gabor时频分布图的频带能量累加曲线中提取能够反映曲轴轴承磨损故障的频带能量作为故障特征参数。试验结果表明,该方法提取的故障特征参数能敏感地反映曲轴轴承的磨损状态,可作为诊断曲轴轴承故障的重要特征量。     

高速动车组低频周期性晃动问题分析与诊断

高速动车组同一车厢的两个转向架同相蛇行运动频率与车体固有上心滚摆频率接近时,车体因共振而出现低频周期性晃动,不仅影响乘坐舒适性,还会造成脱轨.本文根据低频周期性晃车成因,提出一种以车体横向加速度为研究对象的诊断方法.首先设置车体横向加速度阈值,对超阈值处延拓获得可能存在问题区段,然后利用集合经验模态分解法对这些区段车体...     

PHC刚性管桩基础上的闸首底板内力计算方法

为了合理解决复合桩基上的闸首底板内力简化计算问题,分析复合桩基沉降机理,基于应力与沉降分解法给出桩与桩、桩与土、土与桩和土与土的沉陷系数计算式,结合并列铰接地基梁法建立考虑桩-土-结构共同作用的计算模型。依托高石碑船闸工程实例,通过与查表法的对比验证计算程序的可靠性,研究在不同工况时底板单宽弯矩、沉降、地基反力的变化规律。结果表明,该模型计算的桩土分担比与有限元分析结果接近,计算精度可以满足结构简化分析的需要,能合理反映基桩的根数、以及间距与桩长不规则等因素对底板沉降和内力的影响。运营期相对于完建期而言,地基均处于回弹状态、底板弯矩增量有正负值,可优化调整底板宽缝封闭前边墩浇筑高程、回填土高程,使得运营期出现的正负弯矩接近相等。     

基于时间-费用约束的虚拟物流企业项目分解研究

考虑时间和费用约束条件下的虚拟物流企业项目分解.找出虚拟物流网络中的最短时延路,若该路的时延大于时间约束,则虚拟物流企业盟主认为不能在规定时间内完成该虚拟物流项目,拒绝该物流项目;反之,则从所有满足时间约束的路中挑出一条最小费用的路,通过此路进行分解,若时间和费用都满足虚拟物流企业盟主的期望,则接受该虚拟物流项目.并给出一个算例.     

HTN规划中面向多计划生成的顺序任务分解算法

顺序任务分解算法（OTD）是层次任务网规划（HTN）中的一种高效求解算法．由于算法中的计划生成采用一次性回溯机制,每次求解过程只能产生一个可行计划．文中提出了一种能够快速生成多个可行计划的回溯算法．该算法采用分段回溯的计划生成机制,充分利用了求解过程中生成的局部解序列,从而能够一次性地快速生成多个可行计划,为寻求优化的计划和进行计划的评估提供更为有效、灵活的支持．     

一种多模式下考虑排放的交通分配模型及其算法研究

讨论了一类考虑排放的、非可加路径费用下的交通分配问题.在Venigalla等人研究的基础上,进一步完善了考虑不同发动机启动模式下考虑排放的交通分配模型,并采用一种基于路径的、非集计的单纯分解算法求解,然后应用到一个网络实例中进行了数值验证.数值计算结果表明,在求最优解的迭代过程中,基于非集计的单纯分解算法比基于F-W算法的收敛速度快,适用于求解非可加路径费用的大规模交通分配问题.     

Decomposition analysis of decoupling freight transport from economic growth in China

Freight transport industry plays a significant role in boosting economic growth, while also creating many negative externalities for social welfare and the environment. Using data from 1997 to 2017, this study employs the complete decomposition technique to identify the driving forces of freight transport and explores its internal relations with economic growth in China. The decomposition analysis focuses on four factors that are responsible for freight transport: economic activity, industrial structure, transport intensity, and haulage distance. Then, the decoupling index is adopted to examine the decoupling relationship between freight transport and economic growth in China. The results showed that weak decoupling between freight transport and economic growth was the main state during the study period. A strong decoupling effect was identified in 1997–1998, 2012–2013, and 2014–2015, whereas no decoupling effect was observed in 1998–1999, 2008–2009, and 2011–2012. The transport intensity was the most important contributor to the decoupling progress, the industrial structure effect played a role in promoting the decoupling progress over half of the years during the research period, and the haulage distance effect facilitated the decoupling progress in more than one-third of the years examined.     

A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines

The Hilbert-Huang transform (HHT) has been widely applied and recognised as a powerful time-frequency analysis method for nonlinear and non-stationary signals in numerous engineering fields. One of its major challenges is that the HHT is frequently subject to mode mixing in the processing of practical signals such as those of offshore wind turbines, as the frequencies of offshore wind turbines are typically close and contaminated by noise. To address this issue, this paper proposes a new time-frequency analysis method based on single mode function (SMF) decomposition to overcome the mode mixing problem in the structural health monitoring (SHM) of offshore wind turbines. In this approach, the structural vibration signal is first decomposed into a set of window components using complex exponential decomposition. A state-space model is introduced in the signal decomposition to improve the numerical stability of the decomposition, and then a novel window-alignment strategy, named energy gridding, is proposed and the signals are constructed in the corresponding gridding. Furthermore, energy recollection is implemented in each gridding, and the reassembling of these components yields an SMF that is comparable to the intrinsic mode function (IMF) of the HHT, but with a significant improvement in terms of mode mixing. Four case studies are conducted to evaluate the performance of the proposed method. The first case attempts to detect three different frequencies in a simulated time-invariant signal. The second case attempts to test a synthesised signal with segmental time-varying frequencies (each segment contains three different frequencies components). The analysis results in these two cases indicate that mode mixing can be reduced by the proposed method. Furthermore, a synthesised signal with slowly varying frequencies is used. These analysis results demonstrate the effective suppression of non-relevant frequency components using SMF decomposition. In the third case, the experimental data from vortex-induced vibration (VIV) experiments sponsored by the Norwegian Deepwater Programme (NDP) are used to evaluate the proposed SMF decomposition for vibration mode identification. In the final case, field data acquired from an offshore wind turbine foundation and offshore wind turbine are analysed. The mode identification results obtained using SMF decomposition are compared with those produced by the HHT. The comparison demonstrates superior performance of the proposed method in identifying the vibration modes of the VIV experimental and field data.     

基于欠定盲源分离的柴油机曲轴轴承故障诊断方法研究

针对柴油机曲轴轴承振动信号盲源分离的欠定问题,提出了基于相空间重构和动态聚类奇异值分解的适定化方法。首先通过引入广义时间窗的概念确定最佳时间延迟和嵌入维数,重构信号相空间矩阵;然后对其进行奇异值分解,并对奇异值进行动态聚类以确定最佳重构阶数,进而重构得到虚拟观测信号,从而将欠定问题转变为适定或超定;最后利用自适应Parafac方法对原观测信号与虚拟观测信号构成虚拟传播路径进行盲源分离得到有效源信号。仿真结果表明,该方法可有效分离出混合信号中的源信号,并将其应用到柴油机曲轴轴承故障诊断中,诊断准确率提高了18.4%。     

基于多尺度核独立元分析与核极限学习机的柴油机故障诊断

为提高柴油机故障诊断速度和精度,提出了基于改进多尺度核独立元分析与量子粒子群优化核极限学习机的故障诊断方法。首先利用固有时间尺度分解对缸盖振动信号进行多尺度时频分解,并根据故障敏感度参数筛选有效分量以实现振动冲击特征增强;然后利用核独立元分析消除有效分量间的频带混叠,分离故障敏感频带,并提取各频带的AR模型参数、多尺度模糊熵和标准化能量矩构造联合故障特征向量;最后建立基于量子粒子群优化的核极限学习分类器实现柴油机故障诊断。试验结果表明,该方法有效增强了缸盖振动信号中的故障敏感特征,提高了柴油机故障诊断速度和精度,故障分类准确率达到98.45%。