Sparse discriminant manifold projections for bearing fault diagnosis

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文件名称：Sparse discriminant manifold projections for bearing fault diagnosis

文件大小：1.26MB

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更新时间：2022-08-07 04:42:05

Sparse

The monitored vibration signal of bearing is usually nonlinear and nonstationary, and may be corrupted by background noise. Thus, it is very difficult to accurately extract sensitive and reliable characteristics information from the vibration signal to diagnose bearing health conditions. This paper proposes a novel bearing fault diagnosis method based on sparse discriminant manifold projections (SDMP). The SDMP was developed based on sparsity preserving projections, and sparse manifold clustering and embedding.