Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Abstract

As parameter independent yet simple techniques, the energy operator (EO) and its variants have received considerable attention in the field of bearing fault feature detection. However, the performances of these improved EO techniques are subjected to the limited number of EOs, and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction. As a result, the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises. To address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively. Specifically, the proposed strategy is conducted through the following three steps. First, a multi-dimensional information matrix (MDIM) is constructed by performing the higher order energy operator (HOEO) on the analysis signal iteratively. MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region. Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses. Third, the intrinsic manifolds are weighted to recover the fault-related transients. Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and the empirical mode decomposition.

Graphical abstract

Keywords

higher order energy operator / fault diagnosis / manifold learning / rolling element bearing / information fusion

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Acknowledgements

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research was supported by the National Natural Science Foundation of China (Grant Nos. 52172406 and 51875376), the China Postdoctoral Science Foundation (Grant Nos. 2022T150552 and 2021M702752), and the Suzhou Prospective Research Program, China (Grant No. SYG202111), which are highly appreciated by the authors.

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