针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信...针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信号中的噪声等干扰成分去除,降噪重构后的信号经过基于经验模式分解法(Empirical Mode Decomposition,EMD)的希尔伯特-黄变换,实现故障冲击信号的共振解调处理,将低频周期故障调制信号筛选出来,最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。并通过振动测试信号分析,验证了该方法对提取风电机组滚动轴承故障特征的有效性。展开更多
Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations.In this study,we examined the factors contributing to the decline of autumn sea ice concentration.From the Beau...Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations.In this study,we examined the factors contributing to the decline of autumn sea ice concentration.From the Beaufort Sea to the Barents Sea,autumn sea ice concentration has decreased considerably between 1982 and 2020,and the rates of decline were the highest around the Beaufort Sea.We calculated the correlation coefficients between sea ice extent(SIE)anomalies and anomalies of sea surface temperature(SST),surface air temperature(SAT)and specific humidity(SH).Among these coefficients,the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October,which has a value of−0.9446.The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November,which has a value of−0.9436.Among the correlation coefficients between SIE and SST anomalies,the largest absolute value was found in the coefficient for August to October,which has a value of−0.9410.We conducted empirical orthogonal function(EOF)analyses of sea ice,SST,SAT,SH,sea level pressure(SLP)and the wind field for the months where the absolute values of the correlation coefficient were the largest.The first EOFs of SST,SAT and SH account for 39.07%,63.54%and 47.60%of the total variances,respectively,and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea.The corresponding principal component time series also indicate positive trends.The first EOF of SLP explains 41.57%of the total variance.It is mostly negative in the central Arctic.Over the Beaufort,Chukchi and East Siberian seas,the zonal wind weakened while the meridional wind strengthened.Results from the correlation and EOF analyses further verified the effects of the ice-temperature,ice-SH and ice-SLP feedback mechanisms in the Arctic.These mechanisms accelerate melting and decrease the rate of formation of sea ice.In addition,stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region,further promoting Arctic sea ice decline.展开更多
文摘针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信号中的噪声等干扰成分去除,降噪重构后的信号经过基于经验模式分解法(Empirical Mode Decomposition,EMD)的希尔伯特-黄变换,实现故障冲击信号的共振解调处理,将低频周期故障调制信号筛选出来,最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。并通过振动测试信号分析,验证了该方法对提取风电机组滚动轴承故障特征的有效性。
基金the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant no.2018SDKJ0106-1)Open Fund of the Key Laboratory of Ocean Circulation and Waves,Chinese Academy of Sciences(Grant no.KLOCW2003)the Project of Doctoral Found of Qingdao University of Science and Technology(Grant no.210010022746)。
文摘Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations.In this study,we examined the factors contributing to the decline of autumn sea ice concentration.From the Beaufort Sea to the Barents Sea,autumn sea ice concentration has decreased considerably between 1982 and 2020,and the rates of decline were the highest around the Beaufort Sea.We calculated the correlation coefficients between sea ice extent(SIE)anomalies and anomalies of sea surface temperature(SST),surface air temperature(SAT)and specific humidity(SH).Among these coefficients,the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October,which has a value of−0.9446.The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November,which has a value of−0.9436.Among the correlation coefficients between SIE and SST anomalies,the largest absolute value was found in the coefficient for August to October,which has a value of−0.9410.We conducted empirical orthogonal function(EOF)analyses of sea ice,SST,SAT,SH,sea level pressure(SLP)and the wind field for the months where the absolute values of the correlation coefficient were the largest.The first EOFs of SST,SAT and SH account for 39.07%,63.54%and 47.60%of the total variances,respectively,and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea.The corresponding principal component time series also indicate positive trends.The first EOF of SLP explains 41.57%of the total variance.It is mostly negative in the central Arctic.Over the Beaufort,Chukchi and East Siberian seas,the zonal wind weakened while the meridional wind strengthened.Results from the correlation and EOF analyses further verified the effects of the ice-temperature,ice-SH and ice-SLP feedback mechanisms in the Arctic.These mechanisms accelerate melting and decrease the rate of formation of sea ice.In addition,stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region,further promoting Arctic sea ice decline.
