This paper proposes a novel performance degradation assessment method for bearing based on ensemble empirical mode decomposition (EEMD), and Gaussian mixture model (GMM). EEMD is applied to preprocess the nonstationary vibration signals and get the feature space. GMM is utilized to approximate the density distribution of the lower-dimensional feature space processed by principal component analysis (PCA). The confidence value (CV) is calculated based on the overlap between the distribution of the baseline feature space and that of the testing feature space to indicate the performance of the bearing. The experiment results demonstrate the effectiveness of the proposed method.

Issue Section:
Research Papers
Keywords:
ensemble empirical mode decomposition, intrinsic mode function, Gaussian mixture model, bearing performance degradation assessment
Topics:
Algorithms, Bearings, Signals

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