Trends in Temperature Extremes in Association with Weather-Intraseasonal Fluctuations in Eastern China

Trends in the frequencies of four temperature extremes (the occurrence of warm days, cold days, warm nights and cold nights) with respect to a modulated annual cycle (MAC), and those associated exclusively with weather-intraseasonal fluctuations (WIF) in eastern China were investigated based on an updated homogenized daily maximum and minimum temperature dataset for 1960-2008. The Ensemble Empirical Mode Decomposition (EEMD) method was used to isolate the WIF, MAC, and longer-term components from the temperature series. The annual, winter and summer occurrences of warm (cold) nights were found to have increased (decreased) significantly almost everywhere, while those of warm (cold) days have increased (decreased) in northern China (north of 40°N). However, the four temperature extremes associated exclusively with WIF for winter have decreased almost everywhere, while those for summer have decreased in the north but increased in the south. These characteristics agree with changes in the amplitude of WIF. In particular, winter WIF of maximum temperature tended to weaken almost everywhere, especially in eastern coastal areas (by 10%-20%); summer WIF tended to intensify in southern China by 10%-20%. It is notable that in northern China, the occurrence of warm days has increased, even where that associated with WIF has decreased significantly. This suggests that the recent increasing frequency of warm extremes is due to a considerable rise in the mean temperature level, which surpasses the effect of the weakening weather fluctuations in northern China.     

基于整体经验模态分解的信噪比估计方法

为了提高未知样式信号的信噪比估计性能,提出一种基于噪声辅助的信噪比估计新算法,通过固有模态函数（IMF）分量平均周期的变化判断信号与噪声界限,给出了基于噪声辅助估计法的工作原理和流程图,分析了基于噪声辅助估计法的性能。仿真结果表明,基于噪声辅助估计法能够实现盲信号信噪比估计,在0 dB信噪比下均方误差不超过0.2 dB。     

洞庭湖流域极端降水变化特征分析

随着极端降水事件变率增强,洞庭湖流域频繁的极端洪旱事件严重威胁了地区人水和谐。基于洞庭湖流域28个国家气象站1961-2015年的逐日降水资料,采用RClim Dex模型定义阈值来识别极端降水事件,利用线性倾向估计法和集合经验模态分解(EEMD)组合的方法进一步分析了洞庭湖流域极端降水变化特征,由EEMD方法分解得到的各类极端降水指数的3个固有模态函数分量分别表现出3~6a、8~15a和21~27a的准周期。结果表明:洞庭湖流域极端降水频次峰值出现在6月,4-10月份极端降水频次之和占全年的82.7%,南岳和安化为极端降水高发带。洞庭湖流域除持续湿润日数CWD略有减小外,各项极端降水指数均表现出小幅上升趋势。在空间分布上,该流域东南部受山区地形地貌的影响,极端降水呈明显上升趋势。洞庭湖流域极端降水时空格局变化特征分析对流域水资源开发利用和水安全预警具有重要参考价值。     

基于峭度原则的EEMD-MCKD的柔性薄壁轴承故障特征提取

谐波减速器用柔性薄壁轴承运行过程中因内圈长、短轴交替产生周期性冲击成分.当柔性薄壁轴承发生故障后,这种正常的周期性冲击成分和因故障引起的冲击叠加在一起,使得其故障特征提取难度很大.针对这一特点,提出基于峭度原则的EEMD-MCKD的柔性薄壁轴承故障特征提取方法.首先使用集成经验模态分解算法(EEMD)对信号进行预处理,...     

基于特征优选和GA-SVM的滚动轴承智能评估方法

针对滚动轴承等旋转机械设备零部件的退化状态识别问题,研究并提出一种基于支持向量机(support vector machine,SVM)的智能评估方法.对于在线持续输出的轴承振动信号,采用时域方法和集成经验模态分解(ensem-ble empirical mode decomposition,EEMD)能量熵提取轴承特...     

基于TFPW-MK-Pettitt和EEMD的非一致性水文频率计算方法

近年来,受气候变化及人类活动的影响,使水文情势发生变化,导致频率计算中水文资料不再满足一致性的前提假设。为进行非一致性条件下的水文频率计算,提出基于TFPW-MK-Pettitt和EEMD的非一致性水文频率计算方法,首先应用去趋势预置白Mann-Kendall-Pettitt方法对水文序列进行一致性检验;其次应用EEMD方法对非一致水文序列进行修正;最后对修正的序列进行水文频率计算。统计试验和实例证明,TFPW-MK-Pettitt方法适用于我国水文序列的一致性检验;该方法运用于1956~2012年宜昌站径流序列,检验出有下降趋势;运用EEMD方法对该序列进行修正后,频率计算得出各设计值较未修正的设计值小10%左右。     

基于电动机电流和EEMD的伺服旋转轴故障诊断技术研究

提出了一种基于交流伺服电动机电流和EEMD的伺服旋转轴故障诊断方法,研究了电动机电流测试原理,建立电流信息与故障的关联模型,提出了不同的电动机电流获取策略和电动机电流信息的位置及时间表达方法;并针对伺服旋转轴的运行特性,建立了典型的伺服旋转轴机械传动结构动力学模型,深入分析了采用EEMD对伺服旋转轴进行故障诊断的原理。试验结果表明:交流伺服电动机电流信息和EEMD方法用于伺服旋转轴机械传动部件故障诊断的可行性和有效性,从而为其在线监测和故障快速溯源,提供技术支撑。     

An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis

A novel time–frequency analysis method called complementary complete ensemble empirical mode decomposition (EEMD) with adaptive noise (CCEEMDAN) is proposed to analyze nonstationary vibration signals. CCEEMDAN combines the advantages of improved EEMD with adaptive noise and complementary EEMD, and it improves decomposition performance by reducing reconstruction error and mitigating the effect of mode mixing. However, because white noise mixed in with the raw vibration signal covers the whole frequency bandwidth, each mode inevitably contains some mode noise, which can easily inundate the fault-related information. This paper proposes a time–frequency analysis method based on CCEEMDAN and minimum entropy deconvolution (MED) for fault detection of rolling element bearings. First, a raw signal is decomposed into a series of intrinsic mode functions (IMFs) by using the CCEEMDAN method. Then a sensitive parameter (SP) based on adjusted kurtosis and Pearson’s correlation coefficient is applied to select a sensitive mode that contains the most fault-related information. Finally, the MED is applied to enhance the fault-related impulses in the selected IMF. The fault signals of high-speed train axle-box bearing are applied to verify the effectiveness of the proposed method. Results show that the proposed method can effectively reveal axle-bearing defects’ fault information. The comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN. Further, we conducted comparisons that highlight the superiority of our proposed method over individual CCEEMDAN and MED methods and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.     

EEMD和TFPF联合降噪法在齿轮故障诊断中的应用

为了消除噪声对齿轮传动系统故障特征提取的影响,提出了一种基于集成经验模态分解(ensemble empirical mode decomposition,简称EEMD)和时频峰值滤波(time-frequency peak filtering,简称TFPF)相结合的降噪方法。针对TFPF算法在窗长的选择方面受到限制的问题,采用了EEMD方法对其进行改进,使得信号在噪声压制和有效信号保真两方面得到权衡;含噪声的信号经过EEMD分解后,得到一系列频率成分从高到低的本征模态函数(intrinsic mode functions,简称IMFs),计算出各IMFs间的相关系数,判断需要滤波的IMFs。对不同的IMFs选择不同的窗长进行TFPF滤波,把过滤后的IMFs和剩余的IMFs重构得到最终的降噪信号。用模拟仿真信号和齿轮齿根故障信号对该方法进行验证,可见EEMD+TFPF能有效地去除噪声,成功提取齿根裂纹故障特征。     

运用总体经验模态分解的疲劳信号降噪方法

将总体经验模态分解(ensemble empircal mode decomposition,简称EEMD)用于疲劳应变信号降噪,并与小波变换(wavelet transform,简称WT)方法进行了对比.提出了基于EEMD方法的疲劳应变信号降噪计算步骤,并分别用于模拟信号、试验数据和实测资料的降噪处理.讨论了EEMD计算参数对降噪效果的影响,给出了计算参数的选取原则.结果表明,EEMD方法可以较好地降低疲劳信号的噪声,提高应力循环次数统计的准确度,具有自适应的特点.