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Finally, CF is constructed to extract the weaker fault feature signal from resonance components and exclude the interference components.
The magnitudes of signals consisting of resonance frequency and harmonic resonance components are proportional to the momentum transferred from the incident particles to the target sensor.
In RSSD-CF method, the collected signal is firstly separated into the high and the low resonance components through using the RSSD method with the optimal decomposition parameters.
And then, both of the high and the low resonance components are demodulated with the Hilbert transform and the fault information can be found in Hilbert envelop spectra.
A 3D global MHD simulation of the 10 11 January ,1997 event has been analyzed for mode structure and shown to contain field line resonance components, both toroidal and poloidal, with peak power on the nightside during southward IMF conditions.
The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information.
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The RSSD-CF method is based on the RSSD method which can nonlinearly decompose the vibrational signal of the gearbox with multiple faults into the high resonance component and the low resonance component.
The method contains two key procedures, the quasi-steady component separation in angle domain and the impact resonance component extraction in time domain.
Then, the master sub-band is selected out to reconstruct the low resonance component based on the principle of energy dominant distribution.
The kurtosis value of the low resonance component is taken as the objective function to optimize the combination of high and low quality factors with genetic algorithm.
The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra.
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