0
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

## Abstract

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.

<>

## Figures

Fig. 1

Overview of the AE based methodology

Fig. 2

The multiplication of two sinusoid signals

Fig. 3

The extraction of the heterodyned signal by frequency domain filtering

Fig. 4

The drawing of the 6205-2RS ball bearing

Fig. 6

The bearing test rig

Fig. 5

The spectral averaging approach

Fig. 7

The steel bearing seeded fault tests

Fig. 8

Demodulation and sampling devices

Fig. 9

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

Fig. 10

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

Fig. 12

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

Fig. 13

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

Fig. 14

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

Fig. 15

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

Fig. 16

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

Fig. 17

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

Fig. 11

Comparison of the spectral averaging and envelope analysis techniques

Fig. 18

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

Fig. 19

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

## Discussions

Some tools below are only available to our subscribers or users with an online account.

### Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related Proceedings Articles
Related eBook Content
Topic Collections