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Article

Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach

by 1,*, 1 and 1,2
1
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
2
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(7), 317; https://doi.org/10.3390/e19070317
Received: 4 May 2017 / Revised: 16 June 2017 / Accepted: 25 June 2017 / Published: 29 June 2017
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory III)
Aiming at the issue of extracting the incipient single-fault and multi-fault of rotating machinery from the nonlinear and non-stationary vibration signals with a strong background noise, a new fault diagnosis method based on improved autoregressive-Minimum entropy deconvolution (improved AR-MED) and variational mode decomposition (VMD) is proposed. Due to the complexity of rotating machinery systems, the periodic transient impulses of single-fault and multiple-faults always emerge in the acquired vibration signals. The improved autoregressive minimum entropy deconvolution (AR-MED) technique can effectively deconvolve the influence of the background noise, which aims to enhance the peak value of the multiple transient impulses. Nevertheless, the envelope spectrum of simulation and experimental in this work shows that there are many interference components exist on both left and right of fault characteristic frequencies when the background noise is strong. To overcome this shortcoming, the VMD is thus applied to adaptively decompose the filtered output vibration signal into a number of quasi-orthogonal intrinsic modes so as to better detect the single- and multiple-faults via those sub-band signals. The experimental and engineering application results demonstrate that the proposed method dramatically sharpens the fault characteristic frequencies (FCFs) from the impacts of bearing outer race and gearbox faults compared to the traditional methods, which show a significant improvement in early incipient faults of rotating machinery. View Full-Text
Keywords: feature extraction; improved autoregressive minimum entropy deconvolution; variational mode decomposition (VMD); incipient faults; rotating machinery feature extraction; improved autoregressive minimum entropy deconvolution; variational mode decomposition (VMD); incipient faults; rotating machinery
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MDPI and ACS Style

Li, Q.; Ji, X.; Liang, S.Y. Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach. Entropy 2017, 19, 317. https://doi.org/10.3390/e19070317

AMA Style

Li Q, Ji X, Liang SY. Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach. Entropy. 2017; 19(7):317. https://doi.org/10.3390/e19070317

Chicago/Turabian Style

Li, Qing, Xia Ji, and Steven Y. Liang. 2017. "Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach" Entropy 19, no. 7: 317. https://doi.org/10.3390/e19070317

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