Programming Implementation of EMD-Based Rolling Bearing Fault Diagnosis

EMD (Empirical Mode Decomposition)-based rolling bearing fault diagnosis is a widely used signal processing method primarily employed for extracting fault characteristic frequencies from complex vibration signals. The core concept involves decomposing nonlinear and non-stationary signals into a series of Intrinsic Mode Functions (IMFs) to reveal the key frequency components within the signal. The programming implementation typically requires creating functions for signal sifting processes and IMF extraction algorithms.

During implementation, vibration signals from rolling bearings are first acquired - these signals typically contain noise and multiple frequency components. Through the EMD algorithm, the original signal can be progressively decomposed into several IMF components, with each component representing vibration characteristics at different time scales. The decomposition process can be programmed using iterative loops for sifting operations and stopping criteria calculations. After decomposition, the spectral characteristics of IMFs are further analyzed to extract fault-related characteristic frequencies such as inner race, outer race, or rolling element fault frequencies. This spectral analysis often involves implementing Fast Fourier Transform (FFT) functions and frequency peak detection algorithms.

This method effectively handles nonlinear vibration signals commonly encountered in practical engineering applications and doesn't rely on predefined basis functions, making it suitable for bearing fault diagnosis under various operating conditions. By programming the EMD decomposition and subsequent analysis, automated processing of large volumes of vibration data can be achieved, significantly improving the efficiency and accuracy of fault diagnosis through optimized computational algorithms and batch processing capabilities.