Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM
نویسندگان
چکیده
The problem of low digging efficiency and mining imbalance due to outdated technology degree equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key roadheading equipment. Accurate lithology significantly affects automatic control roadheader cutting conditions. Completing process rock wall by a involved following steps: building tunneling experiment platform, making four specimens with different lithologies, completing simulation experiments on obtaining current sensor data lithologies cutting, finally proposing an intelligent method PSO-VMD-LSSVM. research results show that particle swarm algorithm (PSO) optimized variational modal decomposition (VMD) minimum envelope information entropy as fitness function can realize adaptive signal truncated motors. reconstruction increase signal-to-noise ratio selecting eigenmodal components according energy density correlation coefficient criterion. multi-scale fuzzy used eigenvector reconstructed cut-off motor currents better differentiation at scales. least-squares support vector machine (LSSVM) classify feature vectors processed custom parameter VMD gives recognition rate 87.5%. increases 97.5% for PSO-VMD. optimizes noise reduction via effectively improve rate. provide methodological reference property during machine.
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ژورنال
عنوان ژورنال: Frontiers in Earth Science
سال: 2022
ISSN: ['2296-6463']
DOI: https://doi.org/10.3389/feart.2022.884633