Bearing Fault Diagnosis Using Wavelet Packet Analysis

This project implements bearing fault diagnosis using wavelet packet analysis for signal processing and feature extraction, complemented by graphical user interface (GUI) programming. Wavelet packet analysis serves as an advanced signal processing technique that decomposes signals into multiple frequency sub-bands, enabling detailed analysis of each frequency component. The implementation involves applying wavelet packet transform algorithms to vibration signals, where specific frequency bands containing fault characteristics are identified and analyzed through energy distribution calculations. Key technical aspects include: - Wavelet packet decomposition using functions like wpdec() in MATLAB to create full binary tree structures - Feature extraction from terminal nodes using wpcoef() function to obtain detailed frequency band information - Fault identification through energy entropy analysis of different frequency bands - Threshold-based fault detection algorithms comparing normal and faulty bearing signatures The GUI programming component utilizes MATLAB's App Designer or GUIDE framework to create an interactive interface featuring: - Signal visualization panels for raw and processed data - Parameter configuration controls for wavelet selection and decomposition levels - Automated fault diagnostic modules with result display panels - Data import/export functionalities supporting common vibration signal formats This integrated approach enhances fault diagnosis accuracy by capturing subtle frequency variations often missed by traditional FFT analysis, while the user-friendly interface simplifies operational workflows for maintenance engineers and technicians.