Abstract
The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.
Highlights
E fault vibration characteristics from two orthogonal sensors, which are placed in various locations on the machine, have been shown to be important for diagnosing faults
The empirical mode decomposition (EMD) or local mean decomposition (LMD) method is relatively sensitive to noise, which causes the intrinsic mode functions (IMFs) group or product functions (PFs) group frequency to be inconsistent with other IMF or PF groups
The electrical signal was analyzed without considering the noise and the number of bivariate empirical mode decomposition (BEMD) projections. e associated experiments indicated that the number of projection directions affects the decomposition results, when noise is included
Summary
For a mixed signal z(t), the decomposition process based on the BEMD is as follows [5]: Step 1: determine the number of projections N and calculate the projection directions: φn. Step 8: perform sifting process whether the stopping criterion similar to the one proposed in [19] is met; h(t) and m(t) are bivariate signals. Step 10: take r1(t) as the original signal and repeat the above calculation until the second IMF c2(t) is obtained. K 1 where K represents the total number of IMFs. During the decomposition process, the bivariate rotating signal is required to rotate around the zero point. A ratio λ is set, and the ratio between the signal to be decomposed and the energy of the original signal is less than a specific value that serves as a criterion to stop the BEMD algorithm.
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