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.
Highlights
With the deepening of research, a new branch of blasting vibration characteristics has appeared: energy entropy
EEMD algorithm was used to decompose the energy entropy of vibration signal, and the energy characteristics of each IMF are obtained as the input vector of SVM. rough the analysis and training of many kinds of vibration signals, the accurate prediction of vibration types was realized. e research results can provide technical reserves for urban vibration
The frequency aliasing effect of EMD algorithm has been perplexing scholars. e improved idea of EEMD is to add white noise at the beginning of EMD decomposition cycle, so as to solve the problem of frequency aliasing in traditional EMD algorithm. e signal was decomposed by the EEMD algorithm to obtain a plurality of IMFs, which represent the characteristic distribution of the signal energy
Summary
Where E is the total energy of the signal and Ei is the energy of each IMF. In this paper, − Ei/ElogEi/E is called CEE (components of energy entropy). Erefore, the vibration energy entropy can be studied in depth as a characteristic parameter of vibration signal classification. E samples of positive and negative classes on the boundary of interval are support vectors. E standard SVM computing process was used to construct multiple decision boundaries in order to achieve multiclassification of samples [23]. Erefore, the author has carried out many full-scale highpressure natural gas pipeline explosion tests, as shown, and recorded the pipeline explosion vibration data as a kind of sample for this study. Scene 3: surface rock blast vibration: rock blasting is a key method for the rapid and high-quality construction of Signal type
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