Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM

Abstract

In this study, the authors introduced energy entropy as a reference feature into the field of blast vibration recognition classification and achieved good results. On the basis of the previous experimental database, 4 kinds of typical vibration signals were selected to form the sample group (building collapse vibration, surface rock blast vibration, underground tunnel blast vibration, and natural gas pipeline explosion vibration). EEMD (ensemble empirical mode decomposition) algorithm was used to calculate the energy entropy of each signal. Taking eigenvector composed of CEE (components of energy entropy) as input, multiclassification SVM algorithm was used for training and prediction. Prediction accuracy was more than 80%. Compared with BP (backpropagation) neural network algorithm, SVM (support vector machine) algorithm has higher training efficiency. The research results can be used in urban vibration monitoring, identify the nature of vibration source in time, and provide technical support for rapid response of emergency rescue.

Identification of the blasting vibration characteristics of groundwater-sealed tunnel

“…To solve the above-mentioned problems in blasting vibration signal processing, analyse and evaluate the blasting vibration characteristics, safety and stability in groundwater-sealed tunnel, this work employs multi-scale permutation entropy (MPE) as a detection method of signal randomness and dynamic mutation to detect the complexity and randomness of IMFs derived from CEEMDAN decomposition, eliminating noise or spurious components to achieve signal noise reduction and purification, and provide feasibility for the subsequent Hilbert transform 24 . The CEEMDAN-MPE method has much advantages in terms of denoising and adaptive discrimination compared to other methods.…”