This paper puts forward the LPM fault diagnosis method in the view of the important purpose of on-line monitoring and fault diagnosis for hoister brake system. The feasibility of the two diagnosis methods are proved i...This paper puts forward the LPM fault diagnosis method in the view of the important purpose of on-line monitoring and fault diagnosis for hoister brake system. The feasibility of the two diagnosis methods are proved in theories; two methods are proved about feasibility and reliability through testing. Two methods are manifestoed that they can undertake the on-line monitoring and fault diagnosis for hoister brake system with satisfied effect.展开更多
Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （...Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （BSK） is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent in- spection process is divided into three stages： first, the region-of-interest （ROI） is segmented from the source image by an im- proved spatial pyramid matching scheme based on multi-scale census transform （MSCT）. To localize the BSK in the ROI, cen- sus transform （CT） on gradient images is developed in the second stage. Then gradient encoding histogram （GEH） features and linear support vector machines （SVMs） are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.展开更多
Displacement monitoring in open-pit mines is one of the important tasks for safe management of mining processes.Differential interferometric synthetic aperture radar(DInSAR),mounted on an artificial satellite,has the ...Displacement monitoring in open-pit mines is one of the important tasks for safe management of mining processes.Differential interferometric synthetic aperture radar(DInSAR),mounted on an artificial satellite,has the potential to be a cost-effective method for monitoring surface displacements over extensive areas,such as open-pit mines.DInSAR requires the ground surface elevation data in the process of its analysis as a digital elevation model(DEM).However,since the topography of the ground surface in open-pit mines changes largely due to excavations,measurement errors can occur due to insufficient information on the elevation of mining areas.In this paper,effect of different elevation models on the accuracy of the displacement monitoring results by DInSAR is investigated at a limestone quarry.In addition,validity of the DInSAR results using an appropriate DEM is examined by comparing them with the results obtained by global positioning system(GPS)monitoring conducted for three years at the same limestone quarry.It is found that the uncertainty of DEMs induces large errors in the displacement monitoring results if the baseline length of the satellites between the master and the slave data is longer than a few hundred meters.Comparing the monitoring results of DInSAR and GPS,the root mean square error(RMSE)of the discrepancy between the two sets of results is less than 10 mm if an appropriate DEM,considering the excavation processes,is used.It is proven that DInSAR can be applied for monitoring the displacements of mine slopes with centimeter-level accuracy.展开更多
文摘This paper puts forward the LPM fault diagnosis method in the view of the important purpose of on-line monitoring and fault diagnosis for hoister brake system. The feasibility of the two diagnosis methods are proved in theories; two methods are proved about feasibility and reliability through testing. Two methods are manifestoed that they can undertake the on-line monitoring and fault diagnosis for hoister brake system with satisfied effect.
基金supported by the Special-Funded Programme on National Key Scientific Instruments and Equipment Development(No.2012YQ140032)the National Natural Science Foundation of China(No.51179076)+2 种基金the Jiangsu Province Postdoctoral Research Funding Plan(No.1402012B)the Scientific Research Foundation of Jiangsu University for Senior Personnel(No.14JDG134)the Jiangsu Province Science and Technology Support Plan(Industrial)(No.BE2012149)
文摘Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （BSK） is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent in- spection process is divided into three stages： first, the region-of-interest （ROI） is segmented from the source image by an im- proved spatial pyramid matching scheme based on multi-scale census transform （MSCT）. To localize the BSK in the ROI, cen- sus transform （CT） on gradient images is developed in the second stage. Then gradient encoding histogram （GEH） features and linear support vector machines （SVMs） are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.
基金partially supported by JSPS KAKENHI(Grant No.16H03153)the Limestone Association of Japan。
文摘Displacement monitoring in open-pit mines is one of the important tasks for safe management of mining processes.Differential interferometric synthetic aperture radar(DInSAR),mounted on an artificial satellite,has the potential to be a cost-effective method for monitoring surface displacements over extensive areas,such as open-pit mines.DInSAR requires the ground surface elevation data in the process of its analysis as a digital elevation model(DEM).However,since the topography of the ground surface in open-pit mines changes largely due to excavations,measurement errors can occur due to insufficient information on the elevation of mining areas.In this paper,effect of different elevation models on the accuracy of the displacement monitoring results by DInSAR is investigated at a limestone quarry.In addition,validity of the DInSAR results using an appropriate DEM is examined by comparing them with the results obtained by global positioning system(GPS)monitoring conducted for three years at the same limestone quarry.It is found that the uncertainty of DEMs induces large errors in the displacement monitoring results if the baseline length of the satellites between the master and the slave data is longer than a few hundred meters.Comparing the monitoring results of DInSAR and GPS,the root mean square error(RMSE)of the discrepancy between the two sets of results is less than 10 mm if an appropriate DEM,considering the excavation processes,is used.It is proven that DInSAR can be applied for monitoring the displacements of mine slopes with centimeter-level accuracy.
