Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM - Qi, Pengfei; Chang, Jucai; Chen, Xiao; Wang, Tuo; Wu, Mengyun (Frontiers in Earth Science, 2022)
The low degree of intelligent equipment and backward digging technology at the digging working face lead to low working efficiency and tension of alternating mining and digging. Lithology identification is the key factor for efficient excavation, and accurate lithology identification method is of great significance to achieve efficient and safe excavation. By building a tunnelling experimental platform, completing tunnelling simulation experiments under four lithologies, obtaining current sensor data under four lithology cut-offs, and proposing an intelligent lithology identification method of PSO-VMD-LSSVM. The research results show that the particle swarm algorithm (PSO) optimized variational modal decomposition (VMD) with minimum envelope information entropy as the fitness function can realize the adaptive decomposition of truncated motor current signals. The signal reconstruction can increase the signal-to-noise ratio of the current signal by selecting the decomposed components according to the energy density and correlation coefficient criteria. The multi-scale fuzzy entropy is used as the feature vector of the reconstructed current signal, and the fuzzy entropy of different lithology cut-off motor currents has a good differentiation degree under different scales. Finally, the least squares support vector machine (LSSVM) is used to classify the feature vectors, and the recognition rate reaches 97.5%, which finally achieves the purpose of lithology identification. The research results can provide some methodological references for the identification of rock properties in the process of rock wall truncation by roadheading machine.