Abstract

This paper presents a fault diagnosis technique for roller element bearings based on a combined de-trended fluctuation analysis (DFA) and variational mode decomposition (VMD). DFA can reveal the long-range correlation existed within a data series and is used to filter out the uncorrelated trends in a non-stationary bearing condition monitoring (CM) signal and to determine the number of decomposed modes K for VMD. VMD then decomposes the CM signal into K intrinsic mode functions (IMFs) and the fault related IMFs from VMD are selected based on DFA for the re-construction of the filtered signal. The result shows that the de-noised signal after VMD can detect an incipient bearing defect from a low signal-to-noise ratio (SNR) CM data.

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