A hybrid multi-measure and improved UMAP approach for train traction motor bearing protection state assessment

Abstract

Abstract Different protection states have different protection effects on bogie traction motor bearings under road transportation conditions, which directly affects the stability and safety of later vehicle operations. The motor bearing monitoring system usually adopts the time-frequency domain and other indicators to accurately describe its state, and the selection of specific indicators usually originates from the experience of domain experts. To explore the weak and effective features hidden in the data more deeply, this paper researches feature selection and fusion, and proposes a hybrid multi-measure and improved UMAP approach for train traction motor bearing protection state assessment. Firstly, a platform is built to collect multiple sensor signals of traction motor vibration during road transportation and the time-frequency domain feature set is extracted. Then, by comparing the feature extraction effects of 10 single-measure models, a hybrid multi-measure feature selection model and its evaluation indexes, which comprehensively consider the combined distance measure, correlation, and information measure, are constructed to optimize the best hybrid multimetric and the feature set corresponding to the optimal strategy. To perform multi-sensor feature fusion, the PSO-optimized UMAP algorithm is used for feature fusion of the feature set and compared with eight other algorithms. Finally, the results of feature selection and fusion are input into eight classifiers for performance comparison, and the method achieves more than 85% accuracy in distinguishing different protection states, obviously due to the original feature subset. The experimental results prove the effectiveness of the method proposed in this paper, and it provides a theoretical basis and technical reference for the protection scheme of the traction motor and the design and selection of bearings.