Abstract
Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However, this component is very small in the early stage of the fault and masked by various noises such as gear meshing components modulated by shaft frequency, which make it difficult to extract accurately for fault detection. The adaptive line enhancer (ALE) is an effective technique for separating sinusoidals from broad-band components of an input signal for detecting the presence of sinusoids in white noise. In this paper, ALE is explored to suppress the periodical gear meshing frequencies and enhance the fault feature impulses for more accurate fault diagnosis. The results obtained from simulated and experimental vibration signals of a two stage helical gearbox prove that the ALE method is very effective in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency analysis. The results show a clear difference between the baseline and 30% tooth damage of a helical gear which has not been detected successfully in author’s previous studies.
Highlights
Impulsive sound and vibration signals in machinery are often caused by component impacts which are commonly associated with component faults
Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. This component is very small in the early stage of the fault and masked by various noises such as gear meshing components modulated by shaft frequency, which make it difficult to extract accurately for fault detection
The results obtained from simulated and experimental vibration signals of a two stage helical gearbox prove that the adaptive line enhancer (ALE) method is very effective in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency analysis
Summary
Impulsive sound and vibration signals in machinery are often caused by component impacts which are commonly associated with component faults. It tends to be difficult to make objective measurements of impulsive signals because of the high levels of background noise The detection of these impulsive signals is hampered by the presence of the signals associated with the normal running of the machine, with the consequence that the detection of the weak impulsive signals, which are especially associated with incipient faults, is difficult [1]. To reduce the stationary periodic gear meshing noise in gearbox vibration signals, ALE algorithm based on an adaptive LMS filter is examined for early fault detection. In such a way, the periodical noise is cancelled from the signal and impulses contain fault information are highlighted to produce more reliable detection and diagnosis results
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