According to the wavelet transformation theory, a diagnose method for helicopters is established, including the order number of the wavelet decomposition and reconstruction, the noise signal decomposition, the separate signal reconstruction and 1/3 times frequency calculation.
ICA (Independent Component Analysis), which is introduced into the field of process industry as a data analysis method, is a signal decomposing technique based on the higher-order statistical information. This method can utilize the statistical characteristics of the variables more efficiently. The intrinsic characteristics of the process can be described through the decomposing of the monitoring variables under the meanings of the statistical independence.
Independent component analysis (ICA) is a new signal decomposing technique developed in recent years. Based on the statistical independence between com-ponents, the observed mixture signal can be decomposed without any prior infor-mation about the components.
The study has important significance in theory for reasonably to select sensor location,signal sampling frequency and scale of signal decomposing in structural health monitoring based on wavelet analysis.
With this method, the original signal was decomposed into several Intrinsic Mode Functions (IMFs) with EMD, the instantaneous frequency of each IMF was obtained with Hilbert transform, and the spectrum of the instantaneous frequency was accordingly calculated with Fast Fourier Transform (FFT), where the spectrum represented the modulation frequency of corresponding IMF;
The stock daily return time series signal was decomposed on different frequency bands to study the correlativity, and the energy proportions of different frequency components to the original signal were compared.
Firstly,The vibrational signal was decomposed into each frequency band according to wavelet packet analysis,then the energy of each frequency band was identification as characteristic vector to diagnosis structural damage.
The signal was decomposed into high or low frequency elements by wavelet transform. The low frequency elements include the main performances of the signal. But the high frequency elements consist of more noise, reconstructing the signal can be realized after flatting the high frequency elements.
By using the empirical mode decomposition(EMD) method, the signal was decomposed into a finite and small number of intrinsic mode function(IMF), and then the Hilbert transform was applied to each of these intrinsic mode functions to get the energy-frequency-time distribution, designated as the Hilbert spectrum. The energy characteristics of different intrinsic mode functions was extracted to characterize the differential pressure fluctuation signal.
The multi-speed system was changed into single-speed system by frequency domain division and simulated with simulink of MATLAB. The simulated result is discussed and it is turned out that to use the multi-speed system locally can improve the dynamic-character of the system and the stabilization toleration, accelerate the respondence, and reduce the super-accommodate.
In this paper, a new signal decomposition method for analyzing nonstationary or nonlinear data, empirical mode decomposition, is proposed to deal with ultrasonic signals.
A new two-dimensional signal decomposition scheme using the extreme-lifting scheme
This paper discusses the signal decomposition method using the extreme-lifting scheme and two two-dimensional decomposition schemes: separable one-dimensional scheme and two-dimensional scheme with quincunx sampling.
A closed-form wideband direction-of-arrival estimation with chirplet-based adaptive signal decomposition algorithm
Hence, the chirplet-based signal decomposition has attracted great attention in the area of signal processing.