Journal of Signal and Information Processing, 2013, 4, 132-137
doi:10.4236/jsip.2013.43B023 Published Online August 2013 (http://www.scirp.org/journal/jsip)
A Transient Enhancement Method for Two-Stage
Helicopter Gearbox Fault Diagnosis Based on ALE
Xiange Tian
1
, Tie Wang
2
, Zhi Chen
2
, Fengshou Gu
1
, Andrew Ball
1
1
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK;
2
Department of Vehicle Engi-
neering, Taiyuan University of Technology Taiyuan, Shanxi Province, China.
Email: F.Gu@hud.ac.uk, u1178848@hud.ac.uk
Received April, 2013.
ABSTRACT
Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. How-
ever, 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.
Keywords: Adaptive Filter; Adaptive Line Enhancement; Transient Enhancement; Fault Diagnosis
1. Introduction
Impulsive sound and vibration signals in machinery are
often caused by component impacts which are commonly
associated with component faults. It has long been rec-
ognized that the presence of a fault is often indicated by
the presence, or increase in, impulsive signal elements.
However, it tends to be difficult to make objective meas-
urements 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 asso-
ciated 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]. It is the ‘normal’ signals which
form the background noise environment against which
the detection of fault induced impulsive signals must be
conducted. To improve the precision of fault diagnosis, it
is valuable to enhance the impulsive signals by sup-
pressing this background noise prior to further process-
ing.
De-noising and extraction of such faulty signals are
very important for fault diagnostics, especially for early
fault detection, in which the fault features are often very
weak and embedded in noise. Therefore, it is necessary
to enhance the data reliability and improve the accuracy
of the signal analysis. After successful pre-processing the
signal has an increased Signal to Noise Ratio (SNR),
which makes it more amenable to one of a gamut of sig-
nal processing tools which can be used to characterize
the signal, including Auto-Regressive (AR) modeling,
kurtosis evaluation, cepstrum analysis, time-frequency
analysis and higher order spectra analysis [2].
The ALE was introduced in as a method of detecting a
periodic signal in an incoherent background or con-
versely of removing periodic interference from a
broad-band signal of interest [3]. This technique can be
used with any of the adaptive filters classified till now
and uses a delay in the input signal to cancel out the un-
necessary part in it and thus get the desired response.
Naoto Sasaoka etc. applied ALE to reduce the sinusoidal
noise in noise speech signal [4]. J. R. Mohammed etc.
presented one noise reduction system based on two stag-
es of operation with the first stage based on the ALE fil-
ters and the second stage on NLMS (Normalized Least
Mean Square) filter. The first stage reduces the sinusoi-
dal noise from the input signal and the second stage re-
duces the wideband noise [5]. S. K. Lee and P. R. White
exploits two stage ALE filter structures in series to re-
duce the level of background noise. The resulting en-
hanced signals are analyzed in the time-frequency do-
main to obtain simultaneous spectral and temporal in-
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