VIBRATION MONITORING FOR FAULT DIAGNOSIS OF HELICOPTER PLANETRY GEARS
Biqing Wu, Abhinav Saxena, Romano Patrick, and George Vachtsevanos
School of Electrical & Computer Engineering
Georgia Institute of Technology
Atlanta, Georgia 30332-0250
404-894-4132
biqing@ece.gatech.edu
Abstract: In this paper, vibration data analysis techniques are investigated for fault
diagnosis of helicopter planetary gears. A data pre-processing technique is introduced that
achieves the same result as the commonly used Time Synchronous Averaging with much
lower computational complexity since interpolation is not required. A notion of using raw
vibration data instead of the Time Synchronous Averaged data is also presented that is
more suitable for the analysis of vibration data produced by planetary gearboxes and for
the purposes of detecting carrier plate crack fault. Based on this notion, features such as
the Harmonic Index in the frequency domain and the Intra-Revolution Energy Variance in
the wavelet domain are derived. The features are used as inputs to fault classifiers and are
shown to detect the fault successfully based on the test data that is available.
Copyright © 2005 IFAC
Keywords: Fault Diagnosis, Vibration Measurement and Data Analysis, Time
Synchronous Averaging, Planetary Gear Carrier Plate Crack, Feature Extraction.
1. INTRODUCTION
Measurement and analysis of vibration data produced
by a gearbox is important, either with a view to
reducing the noise level of a gearbox or to assessing
its mechanical condition. The majority of the
available data analysis techniques for transmission
systems are based on the Time Synchronous
Averaging (TSA) of vibration signals (McClintic, et
al., 2000; McFadden, 1987, 1989, 1991). It is
assumed that a pulse signal synchronized to the
rotation of a gear indicating the start of an individual
revolution is available. Numerous revolutions are
ensemble averaged resulting in an averaged data with
a length corresponding to a single revolution. The
TSA technique is intended to enhance the vibration
frequencies that are multiples of the shaft frequency,
which in many cases are mainly vibration related to
the meshing of the gear teeth, while averaging out
other components such as random vibrations and
external disturbances. The resulting averaged data
shows vibration characteristics of a gear in the time
domain over one complete revolution, and
differences in the vibration produced by individual
gear teeth can be seen. Advanced local damage to
gear teeth can often be detected by direct inspection
of the time averaged data (McFadden, 1991).
Since the rotational speed of a transmission typically
varies slightly during normal operation, the numbers
of the data samples per revolution are different for a
given sampling frequency. Interpolation is required
to make the sample numbers per revolution the same
before ensemble averaging can be carried out.
Interpolation transforms the vibration signal from the
time domain to the angle domain, and redefines the
sampling frequency to be a function of angular
position rather than time.
In this paper, the TSA technique is compared with
other vibration data pre-processing techniques in the
frequency domain. Since interpolation, as required by
the TSA, is computationally demanding and time-
consuming, it is undesirable especially for on-line
real-time vibration monitoring. A simple technique is
introduced that achieves the same result as the TSA
with much lower computational complexity since
interpolation is not required. The analysis and
comparison between these different preprocessing
techniques are presented in Section 2.
While time domain averaging has proved its superior
performance for the analysis of fixed-axis gear
vibration signals, problems arise when it is applied to
the vibration produced by epicyclic gearboxes. The
problems are mainly due to the motion of the planet
gears and the multiplicity of contact regions between
the planet gears and the annulus gear, and between
the planet gears and the sun gear (Keller and Grabill,
2003; McFadden and Smith, 1985). In (McFadden,
1991), a technique for vibration separation (or
mapping) of each individual planet within one
revolution was used before time averages for the
individual planet gears were calculated. However, the
technique introduces additional complexity and
requires detailed geometrical knowledge of a
planetary transmission system (Forrester, 1998;
Howard, 1991; McFadden, 1994; Samuel and Pines,
2000). Its usefulness is limited to planetary
gearboxes with certain geometric properties, and to
the detection of gear tooth faults.
In this paper novel signal processing and feature
extraction techniques are proposed for analysis of
vibration produced by epicyclic gearboxes that are
based on raw vibration data instead of time
synchronous averaged data. The techniques were
motivated by a crack that was recently found in the
