Abstract
Variational mode decomposition (VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold (Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment (LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method. Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD (RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor (Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability.
Highlights
With the continuous development of industrial modernization and manufacturing informationization, the integration of mechanical equipment and the scale of the system have developed rapidly, posing more severe challenges to fault diagnosis of mechanical equipment
As an important technique to deal with non-stationary signals, time-frequency signal decomposition methods can decompose a complex signal into several regular simple modes that can be analyzed in the time and frequency domain [7]
3.3 Summary of the Improved The multi-bandwidth mode manifold (TM) Method The flowchart of the improved TM method are illustrated in Figure 2, and the specific procedures are briefly described as follows
Summary
With the continuous development of industrial modernization and manufacturing informationization, the integration of mechanical equipment and the scale of the system have developed rapidly, posing more severe challenges to fault diagnosis of mechanical equipment. With the parameters optimized by the above methods, the bearing fault transient components are expected to be extracted with the noise outside the band from the vibration signal by the VMD method [24, 25]. In order to escape the inefficient parameter optimization of the VMD and enable the suppression of in-band noise in the application of bearing fault diagnosis, our group proposed a variation of the VMD, called multi-bandwidth mode manifold (Triple M, TM), by combining the VMD and nonlinear manifold learning [27] This method units a small number of fault-related modes obtained by the recycling VMD (RVMD) with different parameters via a manifold learning algorithm named local tangent space alignment (LTSA). It has been proved that the Bearing vibration signal RVMD with different parameters Construction of multi-bandwidth modes
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