Hierarchical Symbol Transition Entropy: A Novel Feature Extractor for Machinery Health Monitoring

Abstract

This article develops a novel collaborative health monitoring framework based on hierarchical symbol transition entropy (HSTE) and 2-D-extreme learning machine (2-D-ELM) without fusion. In the proposed framework, a novel metric called symbol transition entropy (STE) is first presented to evaluate the dynamical complexity of time series through multistep transition and the joint probability distribution of the symbol state and its transition state. Compared with the existing entropy algorithms, STE has better robustness and captures more detailed dynamical changes. Subsequently, a new feature representation method called HSTE is proposed by combining STE with the hierarchical analysis. The two-order tensor features can be constructed for multichannel data by stacking HSTE values extracted from each single-channel data. Finally, 2-D-ELM is incorporated to identify the extracted two-order tensor features without vectorization. The feasibility of the proposed schemes is verified through simulation and experimental studies, and the final results confirm that the developed schemes have better performance than the existing entropy-based collaborative fault diagnosis methods.