Engine Waveform Analysis Example with Data Files

This example provides analytical data in txt format and primarily employs MATLAB programs to conduct engine waveform analysis using wavelet analysis algorithms, leading to comprehensive engine fault diagnosis. For engine waveform analysis, vibration signals can be collected under various operating conditions. The wavelet analysis algorithm extracts characteristic information including frequency, amplitude, phase, and other relevant parameters from these signals. By analyzing these characteristics, potential engine faults can be identified. Common faults include mechanical wear, electrical malfunctions, combustion issues, and other operational anomalies. Through systematic waveform analysis and fault diagnosis, problems can be detected and resolved promptly, thereby enhancing engine performance and reliability.

Implementation Approach: The MATLAB code typically involves reading vibration data from txt files using functions like readtable() or textscan(), preprocessing signals with wavelet denoising techniques through wdenoise() function, and performing multi-resolution analysis using cwt() or wavedec() functions to extract time-frequency features. Feature extraction may involve calculating statistical parameters and frequency-domain characteristics using fft() and signal processing toolbox functions. Fault classification can be implemented through threshold-based detection or machine learning algorithms like fitcsvm() for pattern recognition.