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The ALIF method can decompose a multi-component AM-FM signal into a number of stationary components.
The EMD method can decompose the original seismic signals into a finite number of Intrinsic Mode Functions (IMFs).
The EMD method can decompose any complicated signal into its IMFs which reflect the intrinsic and reality information of the signal.
LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM FM signal.
The method of multivariate curve resolution with alternating least squares (MCR-ALS) [21 23], as a chemometric method, can decompose the data matrix to profiles (composition profiles and pure elemental distribution profiles) with the use of certain constraints [24 26].
Bearing fault vibration signal belongs to multi-component modulated AM-FM signals, the LMD method can decompose this fault vibration signals into a number of different characteristic time scales product functions (PFs), then the spectrum analysis method is applied to the first PF1 component and the instantaneous frequencies and instantaneous amplitudes are obtained.
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By employing our method we can decompose those racial differences further.
Due to properties of the Gaussian mixture distribution model, the proposed Gaussian mixture based equivalent linearization method (GM-ELM) can decompose the non-Gaussian response of a nonlinear system into multiple Gaussian responses of linear single-degree-of-freedom oscillators.
This time-frequency analysis method can adaptively decompose signals according to their characteristics, thus characteristics of power quality disturbance are automatically extracted from the signals themselves.
As a result and different from previous works that have the dimensions of decomposition pre-defined, our method can adaptively decompose the FFT data on the lowest possible dimensions depending on the number of processes.
Numerical results show that the proposed HHT-based signal decomposition and processing method can accurately decompose nonlinear nonstationary signals and extract accurate intrawave amplitude and phase modulations, distorted harmonic response under a single-frequency harmonic excitation, and different types and orders of nonlinearities.
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