Optimal Symbolic Entropy: an Adaptive Feature Extraction Algorithm for Condition Monitoring of Bearings

Due to their effectiveness in vibration-based fault feature extraction from bearings, entropy-based methods have become a hot research topic. Symbolic dynamic filtering reduces background noise in bearing signals, making it ideal for entropy analysis. However, the partitioning approach selection of symbolic dynamic filtering mainly depends on experience, which may bring over-track and under-track phenomena. The optimal symbolic entropy (OSE) method proposes a solution by using mean spectral kurtosis to evaluate the symbolization performance of partitioning approaches. This method improves the identification of bearing faults through steps such as evaluating frequency and amplitude preservation, selecting the optimal symbolization approach, and using the OSE method with multiscale analysis. Simulative and experimental data analysis demonstrates its superior ability to extract bearing fault characteristics, with better performance and robustness than existing methods.