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
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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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