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Rolling bearings are important load-bearing components that are prone to failure, so it is necessary to reveal their failure mechanisms. As the singular value mainly reflect the energy of the decomposed singular components (SCs), the singular value decomposition (SVD) de-noising tend to preserve the significant-energy SCs and ignoring the neglected one, thus weaken singular has the ability to detect the incipient fault of the mechanical systems. In this paper, accumulative component kurtosis based SVD reconstitution scheme is proposed for mechanical signal denoising to detect the incipient fault of the generator systems. With the kurtosis index, the decomposed SCs can be evaluated and sorted not only according to their energy but also the contribution to the de-noising effect. In this way, the sensitive SCs could be taken into calculation effectively. The advantages of the ACK-SVD over traditional approaches are verified by both simulated signals and real vibration data from the rolling element bearing rig. The results proved the incipient fault feature even with heavy background noise could be extracted successfully.

The Accumulative Component Kurtosis in the SVD Based Denoising of the Fault Diagnosis for Rolling Element Bearing of Generator