MATLAB Code Implementation for Fault Diagnosis Data Processing

In the field of fault diagnosis, MATLAB offers a powerful toolkit for processing and analyzing experimental data. Data obtained through simulation software via iterative testing provides significant reference value, effectively simulating fault characteristics in real-world scenarios.

For such data, the typical processing workflow involves: first performing data preprocessing to eliminate noise and standardize formats using functions like filter() and zscore(); then extracting key features such as time-domain statistics (mean, std) and frequency-domain energy distributions through fft() and pwelch() functions; finally establishing diagnostic models using pattern recognition or machine learning algorithms with tools from Statistics and Machine Learning Toolbox.

MATLAB's advantage lies in its comprehensive signal processing toolbox and concise matrix operation syntax, enabling rapid implementation of the above workflow. For example, employing wavelet transforms (wavedec() function) to analyze non-stationary signals, or using principal component analysis (pca() function) for dimensionality reduction. Multiple validations of simulation data ensure algorithmic robustness, providing reliable foundations for practical applications through cross-validation techniques and confusion matrix analysis.