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Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

Extraction of effective information from measured vibration signals is a fundamental task for the machinery condition monitoring and fault diagnosis. As a typicai blind source separation (BSS) method, independent component analysis (ICA) is known to be abie to effectively extract the latent information in complex signals even when the mixing mode and sources are unknown. In this paper, we propose a novel approach to overcome two major drawbacks of the traditional ICA algorithm: lack of robustness and source contribution evaluation. The enhanced ICA algorithm is established to escalate the separation performance and robustness of ICA algorithm. This algorithm repeatedly separates the mixed signals multiple times with different initial parameters and evaluates the optimal separated components by the clustering evaluation method. Furthermore, the source contributions to the mixed signals can also be evaluated. The effectiveness of the proposed method is validated through the numerical simulation and experiment studies.

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