In this study, the classification threshold of stator slot wedge tightness is established, and a non-destructive testing technology of stator slot wedge tightness based on acoustics is proposed. Firstly, the classification threshold of slot wedge tightness is determined. Then through the compression test of corrugated plate, the corresponding corrugated plate shape variable is obtained. The mechanical classification threshold is transformed into the deformation threshold of corrugated plate. Secondly, the tightness test platform of stator slot wedge is established and the tightness test is carried out. Then, the percussion signals is preprocessed by endpoint detection method, which greatly reduces the subsequent calculation. Subsequently, the characteristic parameters of the percussion signals are extracted by time-domain and frequency-domain analysis, and the characteristic parameters are screened by F-ratio method and Pearson correlation coefficient method. Finally, the Support Vector Machine(SVM) optimized by cross validation method is used to classify the test sets to realize pattern recognition. The results show that the tightness of stator slot wedge can be accurately detected by using this research. This research realizes the intelligent identification of the tightness of the stator slot wedge of the generator set, and provides an important reference for the tightness detection of the stator slot wedge.
This paper proposed method that combined transmission path analysis (TPA) and empirical mode decomposition (EMD) envelope analysis to solve the vibration problem of an industrial robot. Firstly, the deconvolution filter timedomain TPA method is proposed to trace the source along with the time variation. Secondly, the TPA method positioned the main source of robotic vibration under typically different working conditions. Thirdly, independent vibration testing of the Rotate Vector (RV) reducer is conducted under different loads and speeds, which are key components of an industrial robot. The method of EMD and Hilbert envelope was used to extract the fault feature of the RV reducer. Finally, the structural problems of the RV reducer were summarized. The vibration performance of industrial robots was improved through the RV reducer optimization. From the whole industrial robot to the local RV Reducer and then to the internal microstructure of the reducer, the source of defect information is traced accurately. Experimental results showed that the TPA and EMD hybrid methods were more accurate and efficient than traditional time-frequency analysis methods to solve industrial robot vibration problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.