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Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

Gearboxes are widely used in drive systems of rotating machinery. The health status of gearboxes considerably influences the normal and reliable operation of rotating machinery. When a gearbox experiences tooth failure, a vibration signal with impulse features is excited. However, these impulse features tend to be relatively weak and difficult to extract. To solve this problem, a novel approach for gearbox fault feature extraction and fault diagnosis based on improved variational mode extraction (VME) is proposed. Since the initial value of the desired mode center frequency and the value of the penalty parameter in VME must be assigned, a short-time Fourier transform (STFT) was performed, and a new index, the standard deviation of differential values of envelope maxima positions (SDE), is proposed. The feasibility and effectiveness of the proposed approach was verified by a simulation signal and two datasets associated with a gearbox test bench. The results demonstrate that the VME-based approach outperforms the variational mode decomposition (VMD) approach.

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