Code Implementation of Time-Frequency Distribution for Fault Diagnosis Applications

Time-frequency distribution analysis has become a fundamental approach in fault diagnosis applications using MATLAB. This comprehensive codebase enables researchers and engineers to perform sophisticated signal analysis by examining how frequency components evolve over time. The implementation includes various time-frequency representation methods such as Short-Time Fourier Transform (STFT), Wigner-Ville Distribution, and wavelet-based transforms, allowing users to detect transient faults and identify characteristic patterns in mechanical and electrical systems. Through parameter customization features, practitioners can adjust window sizes, overlap percentages, and resolution parameters to optimize fault detection sensitivity. The code structure facilitates batch processing of signal data, automated feature extraction, and visualization of time-frequency contours that reveal fault signatures. By implementing advanced algorithms like empirical mode decomposition (EMD) and Hilbert-Huang transform, the toolkit provides multi-resolution analysis capabilities for both stationary and non-stationary signals. This enhancement leads to more precise fault classification, root cause identification, and ultimately contributes to developing more reliable predictive maintenance strategies for industrial systems.