Code Implementation of Time-Frequency Distribution for Fault Diagnosis Applications

Time-frequency distribution serves as a powerful signal analysis method in fault diagnosis applications, particularly effective for extracting fault features from non-stationary signals. It simultaneously reveals signal variations in both time and frequency domains, enabling engineers to quickly identify early-stage characteristics of mechanical faults.

Implementing time-frequency distribution analysis in MATLAB typically involves these key steps:

Signal Preprocessing The target signal first requires noise reduction and filtering to ensure analysis accuracy. Common methods include wavelet denoising and bandpass filtering. In MATLAB, this can be implemented using functions like wdenoise() for wavelet denoising and designfilt() for creating digital filters. Proper preprocessing significantly enhances feature extraction reliability.

Time-Frequency Transform Selection MATLAB provides multiple time-frequency analysis tools, including Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Wigner-Ville Distribution (WVD). STFT using spectrogram() function suits relatively stationary signals, while CWT (cwt()) and WVD (wvd()) handle non-stationary signals better despite higher computational complexity. The choice depends on signal characteristics and resolution requirements.

Fault Feature Extraction After time-frequency transformation, signal energy distribution patterns become observable. Fault signals typically exhibit abnormal energy concentration in specific frequency bands - for instance, bearing faults may show impact responses in certain high-frequency ranges. MATLAB's tfridge() function can help extract instantaneous frequency features, while time-frequency moments can quantify distribution characteristics.

Fault Diagnosis and Classification Integrating machine learning or pattern recognition algorithms (such as Support Vector Machines or neural networks) enables automated fault classification. By comparing time-frequency features between healthy and faulty signals using Classification Learner app or custom scripts, diagnostic models can be established to improve efficiency. Feature vectors can be created from time-frequency matrices using statistical measures like energy entropy or marginal distributions.

The advantage of time-frequency analysis lies in its intuitive visualization of dynamic signal changes, making it suitable for fault diagnosis in rotating machinery, power systems, bearings, and gearboxes. MATLAB's comprehensive Signal Processing Toolbox facilitates easy implementation and optimization of these methods through functions like pspectrum() for power spectrum analysis and tftool for interactive time-frequency analysis.