Under harmonic wave excitation, the dynamic response of a bilinear SDOF system can be expressed by the Hilbert spectrum. The Hilbert spectrum can be formulated by (1) the inter-wave combination mechanism between the s...Under harmonic wave excitation, the dynamic response of a bilinear SDOF system can be expressed by the Hilbert spectrum. The Hilbert spectrum can be formulated by (1) the inter-wave combination mechanism between the steady response and the transient response when the system behaves linearly, or (2) the intra-wave modulation mechanism embedded in one intrinsic mode function (IMF) component when the system behaves nonlinearly. The temporal variation of the instantaneous frequency of the IMF component is consistent with the system nonlinear behavior of yielding and unloading. As a thorough study of this fundamental structural dynamics problem, this article investigates the influence of the amplitude of the harmonic wave excitation on the Hilbert spectrum and the intrinsic oscillatory mode of the dynamic response of a bilinear SDOF system.展开更多
Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller b...Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.展开更多
DDoS detection has been the research focus in the field of information security. Existing detecting methods such as Hurst parameter method and Markov model must ensure that the network traffic signal f(t) is a station...DDoS detection has been the research focus in the field of information security. Existing detecting methods such as Hurst parameter method and Markov model must ensure that the network traffic signal f(t) is a stationary signal. But its stability is just a regular assumption and has no strict mathematical proof. Therefore methods mentioned above lack of reliable theoretical support. This article introduces Hilbert-HuangTtransformation(HHT) . HHT does not need to be based on signal stability,but it monitors the similarity between Hilbert marginal spectrums of adjacent observation sequences so as to realize DDoS detection. The method is experimented on DARPA 1999 data and simulating data respectively. Experimental results show that the method behaves better than existing Hurst parameter method in distinguishing both the normal and the attacked traffic.展开更多
基金National Natural Science Foundation of China Under Grant No.50278090
文摘Under harmonic wave excitation, the dynamic response of a bilinear SDOF system can be expressed by the Hilbert spectrum. The Hilbert spectrum can be formulated by (1) the inter-wave combination mechanism between the steady response and the transient response when the system behaves linearly, or (2) the intra-wave modulation mechanism embedded in one intrinsic mode function (IMF) component when the system behaves nonlinearly. The temporal variation of the instantaneous frequency of the IMF component is consistent with the system nonlinear behavior of yielding and unloading. As a thorough study of this fundamental structural dynamics problem, this article investigates the influence of the amplitude of the harmonic wave excitation on the Hilbert spectrum and the intrinsic oscillatory mode of the dynamic response of a bilinear SDOF system.
基金This project is supported by National Natural Science Foundation of China (No.50205050).
文摘Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.
基金supported by 410010502, 9140A15060109 DZ802, 9140C1105061005Chinese National Natural Science Foundation (No:61070204)Youth Science Research Fund Project of Beijing University of Technology (No.00700054K4008)
文摘DDoS detection has been the research focus in the field of information security. Existing detecting methods such as Hurst parameter method and Markov model must ensure that the network traffic signal f(t) is a stationary signal. But its stability is just a regular assumption and has no strict mathematical proof. Therefore methods mentioned above lack of reliable theoretical support. This article introduces Hilbert-HuangTtransformation(HHT) . HHT does not need to be based on signal stability,but it monitors the similarity between Hilbert marginal spectrums of adjacent observation sequences so as to realize DDoS detection. The method is experimented on DARPA 1999 data and simulating data respectively. Experimental results show that the method behaves better than existing Hurst parameter method in distinguishing both the normal and the attacked traffic.
