Modified Multi-Scale Symbolic Dynamic Entropy and Fuzzy Broad Learning-Based fast fault diagnosis of Railway Point Machines
نویسندگان
چکیده
Abstract Railway point machine (RPM) condition monitoring has attracted engineers’ attention for safe train operation and accident prevention. To realize the fast accurate fault diagnosis of RPMs, this paper proposes a method based on entropy measurement broad learning system (BLS). Firstly, modified multi-scale symbolic dynamic (MMSDE) module extracts characteristics from collected acoustic signals as features. Then, fuzzy BLS takes above features input to complete model training. Fuzzy introduces Takagi-Sugeno into BLS, which improves model’s classification performance while considering computational speed. Experimental results indicate that proposed significantly reduces running time maintaining high accuracy.
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ژورنال
عنوان ژورنال: Transportation safety and environment
سال: 2022
ISSN: ['2631-4428']
DOI: https://doi.org/10.1093/tse/tdac065