Single-Sensor Identification of Multi-source Vibration Faults Based on Power Spectrum Estimation with Application to Aircraft Engines
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Urban rail
Urban rail transit
SIGNAL (programming language)
Instantaneous phase
Passenger train
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Ensemble Empirical Mode Decomposition (EEMD) can overcome the mode mixing problem in Empirical Mode Decomposition (EMD) effectively. The Hilbert-Huang transform still exists end effect in applications, in order to improve the end effect, this paper put forward a method of fault feature extraction based on improved EEMD and Hilbert transform which combines support vector regression (SVR) machine with mirror extension to continue the signal. The analysis on simulation experiments results show that the method can restrain the end effect effectively, get a more accurate instantaneous frequency and instantaneous amplitude.
Instantaneous phase
Mode (computer interface)
S transform
SIGNAL (programming language)
Feature (linguistics)
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The Empirical Mode Decomposition (EMD) is a new adaptive signal decomposition method, which is good at handling many real nonlinear and nonstationary one dimensional signals. It decomposes signals into a a series of Intrinsic Mode Functions (IMFs) that was shown having better behaved instantaneous frequencies via Hilbert transform (The EMD and Hubert spectrum analysis together were called Hilbert-Huang Transform (HHT) which was proposed by N.E. Huang et al, in.). For the advanced applications in image analysis, the EMD was extended to the bidimensional EMD (BEMD). However, most of the existed BEMD algorithms are slow and have unsatisfied results. In this paper, we firstly proposed a new BEMD algorithm which is comparatively faster and better-performed. Then we use the Riesz transform to get the monogenic signals. The local features (amplitude, phase orientation, phase angle, etc) are evaluated. The simulation results are given in the experiments.
Instantaneous phase
Hilbert spectral analysis
Mode (computer interface)
SIGNAL (programming language)
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Hilbert spectral analysis
Instantaneous phase
S transform
SIGNAL (programming language)
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Hilbert-Huang Transform(HHT) is a new two-step time-frequency analytic method to analyze the non-linear and non-stationary signal.The key step of this method is empirical mode decomposition(EMD) method with which any complicated data set can be decomposed into several Intrinsic Mode Function(IMF) components.Using Hilbert transform to those IMF components can yield instantaneous frequency.The empirical mode decomposition(EMD) can be interpreted as a temporal and spatial filtering based on the signal's extremum characteristic scale.This method preserves the nonlinearity and non-stability of signal,and has potential superiority in filtering and de-noising. The experimental results clarify the advance and efficient of this method.
Instantaneous phase
Hilbert spectral analysis
SIGNAL (programming language)
Mode (computer interface)
Analytic signal
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Hilbert spectral analysis
Mode (computer interface)
SIGNAL (programming language)
Instantaneous phase
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Hilbert spectral analysis
Mode (computer interface)
SIGNAL (programming language)
Instantaneous phase
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Nonlinear and non-stationary signal processing has been always a hot issue.Hilbert-Huang transform is a new signal processing method,which can get the time-frequency-energy distribution characteristics of the signals through the empirical mode decomposition(EMD) and Hilbert transform.But when it is in application,the results gained are not very precise,some improvements are proposed,and it proved feasibility and useful through MATLAB simulation.
Hilbert spectral analysis
SIGNAL (programming language)
Instantaneous phase
S transform
Energy distribution
Mode (computer interface)
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Instantaneous phase
Mode (computer interface)
SIGNAL (programming language)
Hilbert spectral analysis
Seismic Noise
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