Spectrum Of Intrinsic Mode Functions Research Articles

Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM)

This work presents the application of the Hilbert-Huang transform and its marginal spectrum, for the analysis of the stator current signals for bearing faults diagnosis in asynchronous machines. Firstly, the current signals are decomposed into several intrinsic mode functions (IMFs) using the empirical mode decomposition (EMD). The Hilbert Huang spectrum for each IMF is an energy representation in the time-frequency domain using the instantaneous frequency. The marginal spectrum of each IMF can then be obtained. Secondly, the IMFs that includes dominant fault information are modeled using an autoregressive (AR) model. Finally, the AR model parameters serve as the input fault feature vectors to support vector machine (SVM) classifiers. Experimental studies show that the marginal spectrum of the second IMF can be used for the detection and classification of bearing faults. The proposed approach provides a viable signal processing tool for an online machine health status monitoring.

Bivariate EMD‐Based Data Adaptive Approach to the Analysis of Climate Variability

This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India. Fractional Gaussian noise (fGn) is used here as the reference signal. The climate signal and fGn (of same length) are combined to produce bivariate (complex) signal which is decomposed using BEMD into a finite number of sub‐band signals named intrinsic mode functions (IMFs). Both of climate signal as well as fGn are decomposed together into IMFs. The instantaneous frequencies and Fourier spectrum of IMFs are observed to illustrate the property of BEMD. The lowest frequency oscillation of climate signal represents the annual cycle (AC) which is an important factor in analyzing climate change and variability. The energies of the fGn′s IMFs are used to define the data adaptive threshold to separate AC. The IMFs of climate signal with energy exceeding such threshold are summed up to separate the AC. The interannual distance of climate signal is also illustrated for better understanding of climate change and variability.

Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique

A method to analyze the acoustic signals collected in fully-mechanized caving face is presented in this paper. Through analyzing the marginal spectrum and frequency spectrum of intrinsic mode functions obtained by empirical mode decomposition, acoustic signals’ frequency and amplitude characteristics are gotten, that is, high frequency signals about 1000Hz ~2800Hz are produced when the top coal is combined with gangue. Furthermore, the acoustic signals’ instantaneous energy spectrums in the frequency range of 1000Hz ~2800Hz can be used to identify the coal-rock interface.

Detecting characteristics of information masked by a laser-triggered microwave system via Hilbert–Huang transform

A two-wave-mixing microwave system under a delayed feedback control is proposed for chaotic communications in this study. Under the consideration of simple chaotic masking, Hilbert–Huang transform is proved to be an efficient way to detect characteristics of information signals via the spectrum of intrinsic mode functions. Based upon detrended fluctuation as well as multiscale entropy analyses on masking efficiency in the present system, we may suggest that Hilbert–Huang transform would be an alternative method to analyze complex dressed signals from nonlinear optoelectronic systems.