Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of be...Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.展开更多
Soil salinity and ground surface morphology in the Lower Cheliff plain（Algeria） can directly or indirectly impact the stability of environments. Soil salinization in this area is a major pedological problem related ...Soil salinity and ground surface morphology in the Lower Cheliff plain（Algeria） can directly or indirectly impact the stability of environments. Soil salinization in this area is a major pedological problem related to several natural factors, and the topography appears to be important in understanding the spatial distribution of soil salinity. In this study, we analyzed the relationship between topographic parameters and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. Two satellite images of Landsat 7 in winter and summer 2013 with reflectance values and the digital elevation model（DEM） were used. We derived the elevation and slope gradient values from the DEM corresponding to the sampling points in the field. We also calculated the vegetation and soil indices（i.e. NDVI（normalized difference vegetation index）, RVI（ratio vegetation index）, BI（brightness index） and CI（color index）） and soil salinity indices, and analyzed the correlations of soil salinity with topography parameters and the vegetation and soil indices. The results showed that soil salinity had no correlation with slope gradient, while it was significantly correlated with elevation when the EC（electrical conductivity） values were less than 8 d S/m. Also, a good relationship between the spectral bands and measured soil EC was found, leading us to define a new salinity index, i.e. soil adjusted salinity index（SASI）. SASI showed a significant correlation with elevation and measured soil EC values. Finally, we developed a multiple linear regression for soil salinity prediction based on elevation and SASI. With the prediction power of 45%, this model is the first one developed for the study area for soil salinity prediction by the combination of remote sensing and topographic feature analysis.展开更多
基金supported by the National Natural Science Foundation of China (Grant 51205017)the National Science and Technology Support Program (Grant 2015BAG12B01)the National Basic Research Program of China (Grant 2015CB654805)
文摘Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.
文摘Soil salinity and ground surface morphology in the Lower Cheliff plain（Algeria） can directly or indirectly impact the stability of environments. Soil salinization in this area is a major pedological problem related to several natural factors, and the topography appears to be important in understanding the spatial distribution of soil salinity. In this study, we analyzed the relationship between topographic parameters and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. Two satellite images of Landsat 7 in winter and summer 2013 with reflectance values and the digital elevation model（DEM） were used. We derived the elevation and slope gradient values from the DEM corresponding to the sampling points in the field. We also calculated the vegetation and soil indices（i.e. NDVI（normalized difference vegetation index）, RVI（ratio vegetation index）, BI（brightness index） and CI（color index）） and soil salinity indices, and analyzed the correlations of soil salinity with topography parameters and the vegetation and soil indices. The results showed that soil salinity had no correlation with slope gradient, while it was significantly correlated with elevation when the EC（electrical conductivity） values were less than 8 d S/m. Also, a good relationship between the spectral bands and measured soil EC was found, leading us to define a new salinity index, i.e. soil adjusted salinity index（SASI）. SASI showed a significant correlation with elevation and measured soil EC values. Finally, we developed a multiple linear regression for soil salinity prediction based on elevation and SASI. With the prediction power of 45%, this model is the first one developed for the study area for soil salinity prediction by the combination of remote sensing and topographic feature analysis.
