Condition monitoring of a gear box using vibration and acoustic emission based artificial neural network

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

The objective of this study is to investigate and develop an Artificial Neural Network approach based on vibration and AE signals for the detection, and characterization of wear, damage, and malfunction of an experimental gearbox. Five artificial defects were introduced to the gearbox and these are; (1) tooth face wear, (2) full tooth breakage (missing tooth), (3) clearance or backlash, (5) axial gear looseness, and (5) single internal bearing race wear. The signals, collected from extensive experimentation, were analyzed using time-frequency harmonic wavelet transform, Power Spectral Density (PSD), and four statistical measures of the time domain that captured the salient features of the vibration and AE signals. The results of the time and frequency domain analysis were used in developing a neural network-based estimator for on-line monitoring of gearbox operational condition. The results strongly suggest that vibration and acoustic emission (AE) signals have tremendous promise for machine health monitoring and diagnostics. The proposed technique can be adopted for on-line monitoring of power train and Engine systems, which could be a second phase of this project.

Original languageEnglish (US)
DOIs
StatePublished - Dec 1 2001
EventNoise and Vibration Conference and Exposition - Traverse City, MI, United States
Duration: Apr 30 2001May 3 2001

Other

OtherNoise and Vibration Conference and Exposition
CountryUnited States
CityTraverse City, MI
Period4/30/015/3/01

Fingerprint

Condition monitoring
Acoustic emissions
Vibrations (mechanical)
Gears
Wear of materials
Neural networks
Monitoring
Bearings (structural)
Frequency domain analysis
Time domain analysis
Power spectral density
Wavelet transforms
Health
Engines
Defects

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

Cite this

Abu-Mahfouz, I. (2001). Condition monitoring of a gear box using vibration and acoustic emission based artificial neural network. Paper presented at Noise and Vibration Conference and Exposition, Traverse City, MI, United States. https://doi.org/10.4271/2001-01-1484
Abu-Mahfouz, Issam. / Condition monitoring of a gear box using vibration and acoustic emission based artificial neural network. Paper presented at Noise and Vibration Conference and Exposition, Traverse City, MI, United States.
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Abu-Mahfouz, I 2001, 'Condition monitoring of a gear box using vibration and acoustic emission based artificial neural network', Paper presented at Noise and Vibration Conference and Exposition, Traverse City, MI, United States, 4/30/01 - 5/3/01. https://doi.org/10.4271/2001-01-1484

Condition monitoring of a gear box using vibration and acoustic emission based artificial neural network. / Abu-Mahfouz, Issam.

2001. Paper presented at Noise and Vibration Conference and Exposition, Traverse City, MI, United States.

Research output: Contribution to conferencePaper

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AB - The objective of this study is to investigate and develop an Artificial Neural Network approach based on vibration and AE signals for the detection, and characterization of wear, damage, and malfunction of an experimental gearbox. Five artificial defects were introduced to the gearbox and these are; (1) tooth face wear, (2) full tooth breakage (missing tooth), (3) clearance or backlash, (5) axial gear looseness, and (5) single internal bearing race wear. The signals, collected from extensive experimentation, were analyzed using time-frequency harmonic wavelet transform, Power Spectral Density (PSD), and four statistical measures of the time domain that captured the salient features of the vibration and AE signals. The results of the time and frequency domain analysis were used in developing a neural network-based estimator for on-line monitoring of gearbox operational condition. The results strongly suggest that vibration and acoustic emission (AE) signals have tremendous promise for machine health monitoring and diagnostics. The proposed technique can be adopted for on-line monitoring of power train and Engine systems, which could be a second phase of this project.

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Abu-Mahfouz I. Condition monitoring of a gear box using vibration and acoustic emission based artificial neural network. 2001. Paper presented at Noise and Vibration Conference and Exposition, Traverse City, MI, United States. https://doi.org/10.4271/2001-01-1484