@article{ZHANG2026, 
author = {Jian-hua ZHANG and Gan LUO and Gang HUANG and Su-chen JIANG and Bo-yang ZHANG and Wei-tao LIANG},
title = {Intelligent Identification Method for Misfires based on EMD-SVD and Ridge Features},
year = {2026},
journal = {BLASTING},
volume = {43},
number = {3},
pages = {305-314},
keywords = {blasting vibration signals, intelligent misfire monitoring, empirical mode decomposition, singular value denoising, wavelet ridge},
url = {https://www.sciopen.com/article/10.3963/j.issn.1001-487X.2026.03.031},
doi = {10.3963/j.issn.1001-487X.2026.03.031},
abstract = {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.}
}