Abstract:

Rolling element bearing faults account for main causes of rotating machine failures. It is crucial to identify the incipient fault before the bearing steps into serious fault condition. The Hilbert envelope spectrum has been proved powerful and with high practical value to detect transient components in vibration signal but sensitive to noise. Based on the conventional singular value decomposition (SVD) theory, accumulative component kurtosis (ACK) is introduced to de-noising of vibration signal processing. The proposed ACK-SVD emphasizes the accumulative components (ACs) rather than the single singular component (SC) to select the effective SCs to recover signal. The superiority of the ACK-SVD over traditional SVD de-noising is verified by both simulated signals and actual vibration data from two rolling element bearing rigs. The results demonstrate the proposed method can efficiently identify the rolling element bearing faults, especially the early ones with strong background noise.

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