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Sensors 2015, 15(11), 27869-27893; doi:10.3390/s151127869

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

1,2,3
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1,2,* , 1,2
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1
IOT Perception Mine Research Center, China University of Mining and Technology, Xuzhou 221000, China
2
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221000, China
3
School of Medicine Information, Xuzhou Medical College, Xuzhou 221000, China
4
School of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 9 July 2015 / Revised: 29 September 2015 / Accepted: 10 October 2015 / Published: 3 November 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2211 KB, uploaded 3 November 2015]   |  

Abstract

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction. View Full-Text
Keywords: fault diagnosis; Hilbert–Huang Transform; salient features extraction; support vector machine; characteristic frequency bands fault diagnosis; Hilbert–Huang Transform; salient features extraction; support vector machine; characteristic frequency bands
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yu, X.; Ding, E.; Chen, C.; Liu, X.; Li, L. A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform. Sensors 2015, 15, 27869-27893.

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