TY  - JOUR
AU  - ZHANG, Jian-hua
AU  - LUO, Gan
AU  - HUANG, Gang
AU  - JIANG, Su-chen
AU  - ZHANG, Bo-yang
AU  - LIANG, Wei-tao
PY  - 2026
TI  - Intelligent Identification Method for Misfires based on EMD-SVD and Ridge Features
JO  - BLASTING
SN  - 1001-487X
SP  - 305
EP  - 314
VL  - 43
IS  - 3
AB  - Misfire incidents present substantial safety risks in mine blasting operations, making their precise and timely detection critical for operational safety. To address the challenges of low signal-to-noise ratios in complex mine vibration signals and subtle misfires often obscured by noise, this study develops a vibration signal processing method. The approach combines Empirical Mode Decomposition-singular Value Denoising (EMD-SVD) for primary feature extraction with time-segmented wavelet ridge analysis for misfire identification. Applied to a blasting operation case study at a Weinan metal mine, this method successfully identifies misfire events while overcoming the inadequate recognition accuracy of traditional methods in high-noise environments. Field vibration signal analysis confirms the method′s exceptional noise immunity, accurate feature identification, and reliable detection performance, establishing a novel technical solution for real-time intelligent misfire monitoring in mining operations.
UR  - https://doi.org/10.3963/j.issn.1001-487X.2026.03.031
DO  - 10.3963/j.issn.1001-487X.2026.03.031