Signal Decomposition for Fault Analysis Using EEMD

EEMD (Ensemble Empirical Mode Decomposition) is an enhanced version of the Empirical Mode Decomposition method, specifically designed for analyzing nonlinear and non-stationary signals. In fault diagnosis applications, it's commonly employed to decompose vibration or noise signals to extract meaningful fault characteristic information.

### EEMD Signal Decomposition EEMD effectively mitigates the mode mixing problem inherent in traditional EMD by incorporating Gaussian white noise and performing multiple decomposition iterations. The decomposition yields a series of IMFs (Intrinsic Mode Functions), where each IMF represents distinct frequency components of the original signal. In code implementation, this typically involves: - Adding controlled Gaussian noise to the input signal - Performing multiple EMD decompositions - Ensemble averaging the resulting IMF sets Key functions often include noise generation routines and iterative decomposition loops.

### Energy Entropy Extraction Energy entropy serves as a quantitative measure of complexity for each IMF, aiding in fault feature identification. The computational procedure generally involves: - Calculating IMF energy: Computing the squared sum of each IMF component to obtain energy distribution - Normalization processing: Converting energy values into probability distribution form - Entropy calculation: Evaluating the disorder degree of energy distribution using information entropy formulas In algorithmic terms, this translates to array operations for energy computation, normalization functions, and entropy calculation using logarithmic functions.

In fault diagnosis scenarios, variations in energy entropy often correlate with different fault patterns. For instance, bearing wear or gear tooth fractures typically cause significant increases in energy entropy for specific IMFs. By analyzing these characteristic patterns, more accurate fault type identification can be achieved through pattern recognition algorithms or machine learning classifiers.

### Application Advantages The EEMD combined with energy entropy methodology overcomes traditional frequency-domain analysis limitations that require stationary signals, making it particularly suitable for fault detection in rotating machinery under complex operating conditions. The decomposition results demonstrate improved stability, while energy entropy metrics effectively highlight fault-sensitive components through quantitative feature extraction techniques.