Smart multichannel mode extraction for enhanced bearing fault diagnosis.

• SMTS with a solid mathematical theory is built to elegantly detect CFs of the latent modes. • MSD induced by CFs can decompose the multichannel signals by a single-step calculation. • Feature enhancement strategy is designed to locate and fuse multichannel sensitive modes. • SMME method is proposed via combining SMTS, MSD and feature enhancement strategy. • One simulated and two experimental cases are conducted to validate the proposed method. In bearing fault diagnosis, multichannel data can contain more abundant and complete fault information to alleviate the influence of accidental factors in a single channel. To fully employ the fault information concealed in the multichannel data, this paper proposes a smart multichannel mode extraction (SMME) for enhanced bearing fault diagnosis. The SMME method based on multivariate variational mode decomposition (MVMD) and manifold learning overcomes the problems of predefined model parameters in MVMD and shows good performance in mining the intrinsic nonlinear and nonstationary features from multichannel modes of different quality. First, inspired by the convergence property of MVMD, a smart multichannel spectral structure scanner with solid mathematical theory is constructed to adaptively detect the latent center frequencies (CFs) in the multichannel bearing signals without prior knowledge. Second, multichannel single-step decomposition induced by the detected CFs is established to obtain corresponding multichannel modes through only single-step calculation instead of considerable iterations. Third, a fault feature enhancement strategy is designed for locating and fusing the aligned multichannel sensitive modes with different qualities of fault information to highlight the inherent fault features. The superiority of the SMME method for enhanced bearing fault diagnosis in effectiveness and efficiency is proven through simulation and two experiments. [ABSTRACT FROM AUTHOR]