The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibrat...The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.展开更多
The manifold physical signals including micro resistance,infrared thermal signal and acoustic emission signal in the tensile test for double-material friction welding normative samples were monitored and collected dyn...The manifold physical signals including micro resistance,infrared thermal signal and acoustic emission signal in the tensile test for double-material friction welding normative samples were monitored and collected dynamically by TH2512 micro resistance measuring apparatus,flir infrared thermal camera and acoustic emission equipment which possesses 18 bit PCI-2 data acquisition board.Applied acoustic emission and thermal infrared NDT(non-destructive testing) means were used to verify the feasibility of using resistance method and to monitor dynamic damage of the samples.The research of the dynamic monitoring system was carried out with multi-information fusion including resistance,infrared and acoustic emission.The results show that the resistance signal,infrared signal and acoustic emission signal collected synchronously in the injury process of samples have a good mapping.Electrical,thermal and acoustic signals can more accurately capture initiation and development of micro-defects in the sample.Using dynamic micro-resistance method to monitor damage is possible.The method of multi-information fusion monitoring damage possesses higher reliability,which makes the establishing of health condition diagnosing and early warning platform with multiple physical information monitoring possible.展开更多
基金Supported by the National Natural Science Foundation of China(11871131,11671065)the Fundamental Research Funds for the Central Universities(DUT18LK17)
基金Projects(51375484,51475463)supported by the National Natural Science Foundation of ChinaProject(kxk140301)supported by Interdisciplinary Joint Training Project for Doctoral Student of National University of Defense Technology,China
文摘The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.
基金Project(51125023) supported by Distinguished Young Scholars of Natural Science Foundation of ChinaProject(2011CB013405) supported by the National Basic Research Program of China+1 种基金Project supported by China Equipment Maintenance ProgramProject (3120001) supported by the Natural Science Foundation of Beijing,China
文摘The manifold physical signals including micro resistance,infrared thermal signal and acoustic emission signal in the tensile test for double-material friction welding normative samples were monitored and collected dynamically by TH2512 micro resistance measuring apparatus,flir infrared thermal camera and acoustic emission equipment which possesses 18 bit PCI-2 data acquisition board.Applied acoustic emission and thermal infrared NDT(non-destructive testing) means were used to verify the feasibility of using resistance method and to monitor dynamic damage of the samples.The research of the dynamic monitoring system was carried out with multi-information fusion including resistance,infrared and acoustic emission.The results show that the resistance signal,infrared signal and acoustic emission signal collected synchronously in the injury process of samples have a good mapping.Electrical,thermal and acoustic signals can more accurately capture initiation and development of micro-defects in the sample.Using dynamic micro-resistance method to monitor damage is possible.The method of multi-information fusion monitoring damage possesses higher reliability,which makes the establishing of health condition diagnosing and early warning platform with multiple physical information monitoring possible.
