Comprehensive Time-Domain and Frequency-Domain Feature Extraction from Bearing Full Lifecycle Data

This document presents a comprehensive framework for extracting various time-domain and frequency-domain features from bearing full lifecycle data, including 17 distinct time-domain characteristics and 13 frequency-domain parameters. These extracted features serve as critical inputs for bearing lifespan prediction and fault diagnosis systems, enabling optimized maintenance scheduling and enhanced machine protection. The implementation requires sophisticated signal processing algorithms and methods such as: - Wavelet transform decomposition for multi-resolution analysis - Spectral analysis techniques using FFT-based power spectral density calculations - Autoregressive modeling for signal pattern recognition Key computational aspects include: - Time-domain features calculation involving statistical measures (RMS, kurtosis, skewness) - Frequency-domain feature extraction through band energy analysis and spectral centroid computation - Automated feature vector generation using sliding window processing These advanced techniques significantly improve the accuracy and efficiency of feature extraction, thereby providing robust support for bearing health monitoring and maintenance optimization. The methodology incorporates error handling for signal preprocessing and validation checks for feature consistency across different operational conditions.