The extraction of informative vibration components of bearings by different adaptive methods

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

An Improved Empirical Mode Decomposition Method for Vibration Signal

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.

A High Autonomous Sea Front Detection Algorithm Based on SAR Data

This paper has proposed a high autonomous sea front detection algorithm based on SAR data. Through the innovative introduction of empirical mode decomposition method, a good image de-trend and de-stripe effect is achieved. By introducing the calculation of the maximum interclass variance, the automatic conversion of binary images is realized; through the use of polynomial fitting method, the independent screening of front information is realized, and the continuity of front detection results is improved. After comparison, it is found that the new algorithm proposed in this paper has greatly improved detection accuracy and autonomy compared with the old algorithm. Finally, a SAR data of the GF-3 satellite on the west side of Taiwan Island is used to test the new algorithm proposed in this paper. The results show that the detection results are highly consistent with the original image in morphology, and the changes in frontal intensity are also very detailed, verifying the accuracy and autonomy of the new method.

Dynamic Analysis of Criminal Behavior: An Application of Empirical Mode Decomposition

Our objective is to measure the permanent and transitory components of criminality in Brazilian states by using the methodology proposed by At and Chappe (2005). The empirical strategy used follows the Empirical Mode Decomposition method (EMD), proposed by Huang et al. (1998). Based on a sample collected using the Mortality Information System (SIM) from DATASUS, the decomposition process was carried out for the 27 Brazilian states from 1996 to 2015. The results of the decomposition for criminality show that the choice for crime occurs, for the largest part, due to permanent elements, which is a predictor of future crimes over time. The decomposition of criminality into these two types of components establishes some evidence regarding criminal behavior that can serve as reference for policy makers, since the implications of the results found raise questions about the policies to confront and reduce crime.