Abstract
Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.
Highlights
Rolling element bearings are widely used in rotating machinery to support rotating shafts, and the major cause of machinery breakdown is the bearing failure
In order to extract the transient features from the vibration signals, different signal processing techniques have been developed in the area of rotary machine fault diagnosis, such as the wavelet analysis [6], empirical mode decomposition (EMD)
Considering that the vibration signals at early stage defective bearing only have a few resonance bands, we suggest that the number k of the decomposed modes should be set between 2 and 7
Summary
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. The adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). The novel method for enhancing rolling element bearing fault diagnosis is proposed. Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). An idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis
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