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Variational mode decomposition (VMD) has been widely applied to the field of machinery fault diagnosis due to its good time-frequency decomposition capability. However, the performance of the VMD for extraction of fault transient components is dependent on proper parameters in the method and is not satisfactory under noisy conditions. This paper proposes a new method, termed multi-bandwidth mode manifold (Triple M, TM), to extract the real fault transient components for fault diagnosis of rolling bearings. The new TM method unites multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm. The modes with large bandwidths contain more fault-related information while those with small bandwidths contain less fault-unrelated components and noise. The advantages of different fault-related modes are fully used by the proposed method, leading to clear fault transients that facilitates the bearing fault detection. First, an efficiency-improved VMD method named recycling VMD (RVMD) is performed on the bearing signal repeatedly with different parameter values to obtain the fault-related modes with different bandwidths, i.e., the multi-bandwidth modes. Then, the manifold learning algorithm is carried out to extract the intrinsic manifold structure of bearing fault transient components from the multi-bandwidth modes. Finally, the bearing fault type is identified from the extracted feature with clear fault transients. The enhanced performance of the proposed method over the traditional methods is validated by a simulation analysis and two experimental applications of bearing defect identifications.

Multi-Bandwidth Mode Manifold for Fault Diagnosis of Rolling Bearings