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Research Papers

On the Use of Spectral Averaging of Acoustic Emission Signals for Bearing Fault Diagnostics

[+] Author and Article Information
Brandon Van Hecke

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: bvanhe2@uic.edu

David He

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: davidhe@uic.edu

Yongzhi Qu

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: yqu5@uic.edu

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received December 18, 2013; final manuscript received August 12, 2014; published online September 11, 2014. Assoc. Editor: Patrick S. Keogh.

J. Vib. Acoust 136(6), 061009 (Sep 11, 2014) (13 pages) Paper No: VIB-13-1434; doi: 10.1115/1.4028322 History: Received December 18, 2013; Revised August 12, 2014

For years, vibration analysis has been the industry standard for bearing fault diagnosis. However, due to the various advantages over vibration based techniques, the quantification of acoustic emission (AE) for bearing health diagnosis has been an area of interest for recent years. Additionally, most AE based methodologies to date utilize data mining technologies. Presented in this paper is a new approach, combining a heterodyne based frequency reduction technique, time synchronous resampling, and spectral averaging to process AE signals and compute condition indicators (CIs) for bearing fault diagnostics. First, the heterodyne based frequency reduction technique allows the AE signal frequency to be down shifted from several MHz to less than 50 kHz, which approaches that of vibration based methodologies. Next, the sampled AE signals are band pass filtered to retain the useful information related to the bearing defects. Last, a trigger signal is utilized to time synchronously resample the AE signals to allow the calculation of a spectral average and the extraction and evaluation of CIs for bearing fault diagnosis. The technique presented in this paper is validated using the AE signals of seeded fault steel bearings on a bearing test rig. Presented is an effective AE based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage. Moreover, the effectiveness of the presented approach is established through the comparison of both AE and vibration data.

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Figures

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Fig. 1

Overview of the AE based methodology

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Fig. 2

The multiplication of two sinusoid signals

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Fig. 3

The extraction of the heterodyned signal by frequency domain filtering

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Fig. 4

The drawing of the 6205-2RS ball bearing

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Fig. 5

The spectral averaging approach

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Fig. 6

The bearing test rig

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Fig. 7

The steel bearing seeded fault tests

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Fig. 8

Demodulation and sampling devices

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Fig. 9

Inner race fault raw AE and tachometer signal at 30 Hz shaft speed

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Fig. 10

Entropy of band pass filtered healthy bearing signal at a shaft speed of 45 Hz

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Fig. 11

Comparison of the spectral averaging and envelope analysis techniques

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Fig. 12

AE RMS by sample number using spectral average (left) and envelope analysis (right)

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Fig. 13

AE average RMS by shaft speed using spectral average (left) and envelope analysis (right)

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Fig. 14

AE peak by sample number using spectral average (left) and envelope analysis (right)

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Fig. 15

AE average peak by shaft speed using spectral average (left) and envelope analysis (right)

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Fig. 16

RMS by sample number for AE (left) and vibration (right) using spectral averaging

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Fig. 17

Average RMS by shaft speed for AE (left) and vibration (right) using spectral averaging

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Fig. 18

Peak by sample number for AE (left) and vibration (right) using spectral averaging

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Fig. 19

Average peak by shaft speed for AE (left) and vibration (right) using spectral averaging

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