MATLAB Implementation of HHT (Hilbert-Huang Transform) for Signal Analysis

The HHT (Hilbert-Huang Transform) is a signal analysis method based on Empirical Mode Decomposition (EMD). It decomposes signals into multiple Intrinsic Mode Functions (IMFs) through an iterative sifting process that extracts oscillatory components at different time scales. The fundamental principle involves breaking down a signal into a series of IMFs, where each IMF represents a distinct local oscillation mode with meaningful instantaneous frequency characteristics. By analyzing these IMFs using the Hilbert transform, one can obtain detailed time-frequency representations and gain deep insights into signal features and variation patterns. In MATLAB implementation, key steps include: 1. Applying the EMD algorithm to generate IMFs through repeated extraction of local maxima/minima and envelope fitting 2. Using the Hilbert transform on each IMF to compute instantaneous frequency and amplitude 3. Visualizing results through Hilbert spectrum plots This makes HHT particularly valuable for analyzing non-stationary and nonlinear signals, with wide applications in vibration analysis, biomedical signal processing, and fault detection systems. The method's adaptive decomposition nature avoids the fixed basis limitations of traditional Fourier and wavelet transforms.