Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extrac...Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.展开更多
The response of a nonlinear vibration system may be of three types, namely, periodic, quasiperiodic or chaotic,,when the parameters of the system are changed. The periodic motions can be identified by Poincare map, an...The response of a nonlinear vibration system may be of three types, namely, periodic, quasiperiodic or chaotic,,when the parameters of the system are changed. The periodic motions can be identified by Poincare map, and harmonic wavelet transform (HWT) can distinguish quasiperiod from chaos, so the existing domains of different types of motions of the system can be revealed in the parametric space with the method of HWT joining with Poincare map.展开更多
For applications requiring low-power,low-voltage and real-time,a novel analog VLSI implementation of continuous Marr wavelet transform based on CMOS log-domain integrator is proposed. Marr wavelet is approximated by a...For applications requiring low-power,low-voltage and real-time,a novel analog VLSI implementation of continuous Marr wavelet transform based on CMOS log-domain integrator is proposed. Marr wavelet is approximated by a parameterized class of function and with Levenbery-Marquardt nonlinear least square method,the optimum parameters of this function are obtained. The circuits of implementating Marr wavelet transform are composed of analog filter whose impulse response is the required wavelet. The filter design is based on IFLF structure with CMOS log-domain integrators as the main building blocks. SPICE simulations indicate an excellent approximations of ideal wavelet.展开更多
With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of...With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.展开更多
A new theory for inverse problem of wave equation, that is, the union method for scattered wave extrapolation and velocity imaging, is proposed in this paper. This method is very different from the classical wave extr...A new theory for inverse problem of wave equation, that is, the union method for scattered wave extrapolation and velocity imaging, is proposed in this paper. This method is very different from the classical wave extrapolation for migration, because we relate directly the scattered wave extrapolation to velocity inversion. And also this method is different from any linearized inverse method of wave equation, because we needn′t use linearized approximation. Because of this, the method can be applied to strong scattering case effectively (i.e. the value of scattered wave is not small, which can not be neglected). This method, of course, is different from nonlinearized optimum inverse method, because in this paper, the nonlinear inverse problem is turned into two steps inverse problem, i.e. scattered wave extrapolated and velocity imaging, which can be solved easily. Hence, the problem how to get the global optimum solution by using the nonlinearized optimum inverse method doesn′t bother us by using the method in this paper.展开更多
基金supported financially by FundamentalResearch Program of Shanxi Province(No.202103021223056).
文摘Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.
文摘The response of a nonlinear vibration system may be of three types, namely, periodic, quasiperiodic or chaotic,,when the parameters of the system are changed. The periodic motions can be identified by Poincare map, and harmonic wavelet transform (HWT) can distinguish quasiperiod from chaos, so the existing domains of different types of motions of the system can be revealed in the parametric space with the method of HWT joining with Poincare map.
文摘For applications requiring low-power,low-voltage and real-time,a novel analog VLSI implementation of continuous Marr wavelet transform based on CMOS log-domain integrator is proposed. Marr wavelet is approximated by a parameterized class of function and with Levenbery-Marquardt nonlinear least square method,the optimum parameters of this function are obtained. The circuits of implementating Marr wavelet transform are composed of analog filter whose impulse response is the required wavelet. The filter design is based on IFLF structure with CMOS log-domain integrators as the main building blocks. SPICE simulations indicate an excellent approximations of ideal wavelet.
基金This paper is supported by the following funds:National Key R&D Program of China(2018YFF01010100)National natural science foundation of China(61672064),Beijing natural science foundation project(4172001)Advanced information network Beijing laboratory(PXM2019_014204_500029).
文摘With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.
文摘A new theory for inverse problem of wave equation, that is, the union method for scattered wave extrapolation and velocity imaging, is proposed in this paper. This method is very different from the classical wave extrapolation for migration, because we relate directly the scattered wave extrapolation to velocity inversion. And also this method is different from any linearized inverse method of wave equation, because we needn′t use linearized approximation. Because of this, the method can be applied to strong scattering case effectively (i.e. the value of scattered wave is not small, which can not be neglected). This method, of course, is different from nonlinearized optimum inverse method, because in this paper, the nonlinear inverse problem is turned into two steps inverse problem, i.e. scattered wave extrapolated and velocity imaging, which can be solved easily. Hence, the problem how to get the global optimum solution by using the nonlinearized optimum inverse method doesn′t bother us by using the method in this paper.
基金江苏省社会发展科技项目 the Societal Development Research Project of Jiangsu Province China under Grant No.BS2007058) +1 种基金河海大学科技创新基金(No.06B002-02)常州市国家高新区科技项目(No.XE120060408)
