Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors – A Comparative Study

بحث بعنوان
Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors – A Comparative Study
Yongzhi Qu 1, David He 1,*, Jae Yoon 1, Brandon Van Hecke 1, Eric Bechhoefer 2
and Junda Zhu 3
1 Department of Mechanical and Industrial Engineering, University of Illinois at Chicago,
Chicago, IL 60607, USA; E-Mails: [email protected] (Y.Q.); [email protected] (J.Y.);
[email protected] (B.V.H.) 2
Green Power Monitoring Systems, LLC, Essex Junction, VT 05452, USA;
E-Mail: [email protected]
3 Renewable NRG Systems, Hinesburg, VT 05461, USA; E-Mail: [email protected]
Abstract: In recent years, acoustic emission (AE) sensors and AE-based techniques have
been developed and tested for gearbox fault diagnosis. In general, AE-based techniques
require much higher sampling rates than vibration analysis-based techniques for gearbox
fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a
better or at least the same performance as the vibration analysis-based techniques using the
same sampling rate. To answer the question, this paper presents a comparative study for
gearbox tooth damage level diagnostics using AE and vibration measurements, the first
known attempt to compare the gearbox fault diagnostic performance of AE- and vibration
analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded
in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the
AE-based approach has the potential to differentiate gear tooth damage levels in
comparison with the vibration-based approach. While vibration signals are easily affected
by mechanical resonance, the AE signals show more stable performance.
Keywords: gearbox faults; diagnostics; acoustic emission sensor; vibration sensor
Conclusions
Previous research has showed that AE sensor-based approaches using a sampling rate that is
comparable to that of vibration analysis gave good gear fault diagnostic results. However, it is
questionable whether an AE-based technique would give a better or at least the same performance as
the vibration analysis-based techniques using the same sampling rate. To answer the question, this
paper presented a comparative study for gearbox tooth damage level diagnostics using AE and
vibration measurements. Three different levels of tooth cut faults were artificially created and tested on
a notational split torque gearbox in a laboratory. For the AE-based gear fault diagnostic approach, a
hardware frequency convertor based on heterodyne technique was used for AE data collection. Both
the AE signals and vibration signals were collected with the same sampling rate of 100 kHz. Time
synchronous averaging was applied to both types of signals. Condition indicators were then calculated
respectively for AE and vibration signals. Experimental results were provided and explained. Based on
the experimental results, the following conclusions can be drawn:

1. AE signals could be sampled at 100 kHz while maintaining the capability of
   distinguishing tooth damage levels using TSA RMS and P2P.
2. AE signals are insensitive to mechanical background noise and mechanical resonance.
   Therefore, AE signals have the potential to provide better condition indicators for gear
   fault diagnosis.
3. Vibration signal condition indicators are not consistent with gear tooth damage level.
   Vibration is less sensitive than AE to small tooth damage in the low speed range.

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