Abstract
With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.
Highlights
With the development of the information age, signal processing plays an important role in industry and scientific research
The signal is no longer considered the combination of the signature weight value and the cosine total, the base function is not predetermined, and the signal is not used for decomposition according to the signal characteristics
The process can be equivalent to an iterative high pass filter, and the intrinsic mode functions (IMF) can be regarded as a locally narrow-band high-frequency signal component at the end of the iteration
Summary
With the development of the information age, signal processing plays an important role in industry and scientific research. It is necessary to identify useful information, such as physical characteristics and statistical characteristics This process is called signal feature extraction. Decomposition of the empirical mode is a new adaptive method of signal processing. The signal is no longer considered the combination of the signature weight value and the cosine total, the base function is not predetermined, and the signal is not used for decomposition according to the signal characteristics Because it is a dictionary knowledge, it has good adaptability and is suitable for the analysis and processing of nonlinear and abnormal signals. Van and Kang proposed a wind speed prediction method that combines empirical mode decomposition (EMD) and support vector regression (SVR). (1) In order to achieve the final result, it is planned to improve the EMD algorithm to achieve the suppression effect (2) Analyze the analysis of ground motion signals with EMD algorithm
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