Abstract
Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.
Highlights
The analysis of time-frequency of vibration signals is one of the most effective and important methods for fault diagnosis of rotating machinery, since the vibration signal includes massive information that reflects the running state of rotating machinery [1]
Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD
The structure of the paper is as follows: Section 2 introduces the basic principles of EMD; Section 3 narrates the proposed sifting stop criteria based on valid data segment and compares it with the original one; in Section 4, the principle and steps of AEMD are expounded firstly, applied to decompose simulation signals and a rotor vibration signal, and it is compared with the EMD and EEMD methods; Section 5 draws a brief conclusion of current work
Summary
The analysis of time-frequency of vibration signals is one of the most effective and important methods for fault diagnosis of rotating machinery, since the vibration signal includes massive information that reflects the running state of rotating machinery [1]. A new sifting stop criterion was proposed based on valid data segments to solve the problem of sifting stop criteria in the EMD, and an improved method, namely AEMD, combined the analysis mode decomposition (AMD) and EMD. The structure of the paper is as follows: Section 2 introduces the basic principles of EMD; Section 3 narrates the proposed sifting stop criteria based on valid data segment and compares it with the original one; in Section 4, the principle and steps of AEMD are expounded firstly, applied to decompose simulation signals and a rotor vibration signal, and it is compared with the EMD and EEMD methods; Section 5 draws a brief conclusion of current work
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