Vibration Diagnosis and Optimization of Industrial Robot Based on TPA and EMD Methods

This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof 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 themain 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.

Classification of flaw by adaptive filtering deconvolution of ultrasonic backscattering echo

A convolution model of flaw scattering echoes and an adaptive filtering deconvolution method are presented. The effect of the method is analyzed by simulating a given system. By deconvolution, the influence of the transducer on echoes is reduced greatly and the flaw features stand out more clearly in the deconvolved echoes than in flaw echoes themselves. flaw echo signals of 18 flaw samples are processed by adaptive filtering deconvolution. As a result, flaws are classified successfully

Rapid diagnosis of femtosecond laser filament by single laser shot-induced acoustic pulses[Invited]

Due to the promising applications of femtosecond laser filamentation in remote sensing,great demands exist for diagnosing the spatiotemporal dynamics of filamentation.However,until now,the rapid and accurate diagnosis of a femtosecond laser filament remains a severe challenge.Here,a novel filament diagnosing method is proposed,which can measure the longitudinal spatial distribution of the filament by a single laser shot-induced acoustic pulse.The dependences of the point-like plasma acoustic emission on the detection distance and angle are obtained experimentally.The results indicate that the temporal profile of the acoustic wave is independent of the detection distance and detection angle.Using the measured relation among the acoustic emission and the detection distance and angle,a single measurement of the acoustic emission generated by a single laser pulse can diagnose the spatial distribution of the laser filament through the Wiener filter deconvolution(WFD)algorithm.The results obtained by this method are in good agreement with those of traditional point-by-point acoustic diagnosis methods.These findings provide a new solution and idea for the rapid diagnosis of filament,thereby laying a firm foundation for femtosecond laser filament-based promising applications.