gearbox fault features extraction using vibration measurements and novel adaptive filtering scheme

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ID: 197598
2012
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Abstract
Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio), which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal) and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.
Reference Key
ibrahim2012advancesgearbox Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ghalib R. Ibrahim;A. Albarbar
Journal malaria journal
Year 2012
DOI
10.1155/2012/283535
URL
http://dx.doi.org/10.1155/2012/283535
Keywords
acoustics sound

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