Abstract
Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.
Highlights
Rolling bearings are of vital importance in rotary machinery systems, and they are prone to failures due to the complex running conditions [1]
This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings
This paper proposes a novel approach called AHLP denoising method aiming at fault diagnosis of rolling bearings
Summary
Rolling bearings are of vital importance in rotary machinery systems, and they are prone to failures due to the complex running conditions [1]. Smooth orthogonal decomposition (SOD) [24,25,26] projects the points in a high-dimensional phase space into different subspaces to reduce noise, and characteristic frequencies of the original time series can be extracted. By employing high-order polynomials to calculate the centroid of neighborhood during the LP procedures, the HLP was firstly adopted to denoise the vibration signals of faulty rolling bearing to extract its fault characteristic frequencies. In the proposed AHLP denoising method, except adopting high-order polynomials to calculate the centroid of neighborhood, optimal orders of dealing with different kinds of rolling bearing faults can be further estimated to achieve a better denoising effect.
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