Papers by Keyword: Energy Spectrum

Abstract: The model representations of the mechanism of electronic conductivity through InSb nanoparticle was formulated and analyzed in accordance with the theory of field emission. Based on the analysis of tunneling current-voltage characteristics of various methods of calculation obtained a consistent value of characteristic size InSb nanoparticles in the range from 18 to 22 nm. Analysis tunneling current-voltage characteristics (dark and light (stationary light)) allowed us to evaluate the legitimacy of the formulated model representations.

Abstract: In this paper, a new method using wavelet packet energy spectrum to identify transformer inrush and internal fault current was proposed. Wavelet packet transform respectively decomposed inrush and fault current, then calculated the energy of each frequency band of decomposed signal. Finally, the conclusion showed that the energy of internal fault in high frequency band is much less than that of inrush. The difference was quantified and it could be observed intuitively. Through extensive simulations, this paper proves that the method in terms of identifying transformer inrush is accurate and effective.

Abstract: Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

Abstract: When a fault appeared in a power cable transmission line, the transient current with high frequencies would be produced in the system. Three independent mode components could be obtained by applying the phase mode transformation to the transient current. For different types of the faults, the three independent mode components have different features. Based on wavelet energy spectrum of mode components, a method for cable fault recognition is developed in this paper. First, the fault current is decomposed by using Karenbaue transformation matrix. Then, wavelet transformation is uses to obtain the coefficients of the high frequency components which reflect the original signal high frequency energy. Finally, based on the wavelet energy spectrum method and the detailed coefficient manipulation, the equivalent norms of the mode components are obtained. Compared with the traditional fault recognition method, the new method depends less on zero mode component in two-phase short to ground state, and it can recognize the fault class in the cases of different fault positions, different fault path resistances and different inception angles.

Abstract: To diagnosis vibration signals of micro motor in several different fault types a method based on wavelet packet energy spectrum is presented, the energy on each Sub-frequency band, which are Calculated by Wavelet packet decomposition and reconstruction algorithm, are used to normalization process.Under both circumstances of normal working and unmoral working of mechanical equipment,there exist evident differences among the Sub-frequency band energy after the decomposition of wavelet packet, which energy contains a wealth of micro motor running status information and the eigenvectors is structured by the Sub-frequency band energy spectrum can establish energy and Fault mapping relationship.The preliminary experimental results show that it is effective to use the wavelet packet-energy spectrum in micro motor fault diagnosis .

Abstract: The traditional energy response spectra depend on the single-directional earthquake excitations and two dimensional dynamic analyses, in which the imbalance and coupled characteristics of multi-dimensional responses of structures are not considered. So, the energy spectra of constant ductility facotors are developed by considering the single-mass bi-degrees of freedom systems subjected to perpendicular bi-directional earthquake motions. The restoring force rule of single-mass bi-degrees of freedom systems is assumed as two dimensional yield-surface plasticity function. The relative energy spectra for input energy, hysteretic energy, kinetic energy and damping energy are presented and analyzed based on 34 pair earthquake motion records of hard soil site. Analytic results show that the energy spectra for hard soil site have their own characteristics, and the spectral forms and values are affected strongly by vibration period, ductility factor and period ratio of single-mass bi-degrees of freedom systems.

Abstract: It is common for the imbalance-crack coupling fault in rotating machinery, while the crack information is often overshadowed by unbalanced fault information, which is difficult to extract the crack signal. In order to extract the crack signal of the imbalance-crack coupling fault, and realize the fault diagnosis, the paper mainly analyzes its mechanical properties, and then use wavelet packet to de-nosing, decomposing and reconstructing the acquisition of vibration acceleration signal, and then analyzing the characteristics of frequency domain of the fault signal by using the energy spectrum. So the experiment proved that analyze and dispose the acquisition of the fault signal by using the method of the energy spectrum and the wavelet packet, which can effectively distinguish between the crack signal and unbalanced signals in imbalance-crack coupling faults .It also can provide some reference for the diagnosis and prevention for such fault.

Abstract: Based on the study of the characteristics of load current signal, this article develops a method to extract features that can be use to distinguish the different working status of machine tools in real-time manner. The features are extracted from wavelet packet energy spectrum and bispectrum of the load current signal, and thus can take advantages of both wavelet packet transforms and bispectrum in signal analysis. Experimental results show that, compared with the features extracted from wavelet packet energy spectrum or bispectrum alone, the features extracted by applying the proposed method can provide better performance in term of identifying the machine working status.

Abstract: The recognition of coal-rock interface in the top caving was investigated via the vibration signals of the tail beam of the hydraulic support. Wavelet packet transform was used to process the vibration signals. A newly feature based on wavelet packet energy spectrum was proposed to identify the coal-rock interface in top coal caving. The interface was determined by comparing the features in the two cases of coal dropping and rock dropping. The experimental results show that the energy proportion of vibration signal in high frequency bands when rock dropping is greater than that when coal dropping.

Abstract: We investigated the energy spectra in p+n 6H-SiC diodes by a wide variety of charged particles with energies up to several hundred MeV. Though Pulse Height Defect (PHD) was detected when the samples were irradiated with high energy heavy ions (322MeV-Kr and 454MeV-Xe), linearity between pulse height (peak channel) and ion energy up to 150MeV was observed.