The ball bearings of an aero-engine are key parts that frequently fail, and it is very important to effectively carry out fault diagnosis of the ball bearings. However, in the present research work, the ball bearing faults characteristics are extracted mainly from the bearing house signals, it is well known that usually only the casing signals can be measured in practical aero-engine test, and the ball bearing faults characteristics will greatly weaken after transmitting to the casing from the bearing house, therefore, it is very important to extract the fault characteristics of ball bearings from casing vibration signals for the ball bearing fault diagnosis in the practical aero-engine. In this study, simulation experiments for ball bearing faults are conducted using two rotor experimental rigs with casings. In addition, by means of the impulse response method, the transfer characteristics from the ball bearings to casing measuring points are measured, and a sensitivity analysis is performed. Faults are created on the inner ring, outer ring, and ball of the ball bearings in the two experimental rigs. The ball bearing experiments are carried out, and the fault features are extracted by means of a wavelet envelope analysis. The experimental results indicate that, with high connection stiffness between the bearing house and the casing, there is little vibration attenuation. However, with low connection stiffness, the vibration attenuation is great. After the impulse vibrations caused by the ball bearing faults are transmitted to the casing, the casing vibration is very weak and is often submerged in other signals. However, the ball bearing fault characteristic frequencies can still be effectively extracted from the weak casing vibration signals by using a wavelet envelope analysis. The research results in this study provide an experimental basis for a ball bearing fault diagnosis based on a casing test signal from a practical aero-engine.

References

1.
Qiu
,
H.
,
Lee
,
J.
,
Lin
,
J.
, and
Yu
,
G.
,
2006
, “
Wavelet Filter-Based Weak Signature Detection Method and Its Application on Rolling Element Bearing Prognostics
,”
J. Sound Vib.
,
289
(
4
), pp.
1066
1090
.10.1016/j.jsv.2005.03.007
2.
Chen
,
G.
,
2009
, “
Feature Extraction and Intelligent Diagnosis for Ball Bearing Early Faults
,”
Acta Aeronaut. Astronaut. Sin.
,
30
(
2
), pp.
362
367
(in Chinese).100026893(2009)0220362206
3.
Su
,
W. S.
,
Wang
,
F. T.
,
Zhu
,
H.
, et al.,
2010
, “
Rolling Element Bearing Faults Diagnosis Based on Optimal Morlet Wavelet Filter and Autocorrelation Enhancement
,”
Mech. Syst. Signal Process.
,
24
(
5
), pp.
1458
1472
.10.1016/j.ymssp.2009.11.011
4.
Cheng
,
J. S.
,
Yu
,
D. J.
, and
Yang
,
Y.
,
2007
, “
Application of an Impulse Response Wavelet to Fault Diagnosis of Rolling Bearings
,”
Mech. Syst. Signal Process.
,
21
(
2
), pp.
920
929
.10.1016/j.ymssp.2005.09.005
5.
Chen
,
J. L.
,
Zi
,
Y. Y.
,
He
,
Z. J.
, et al.,
2012
, “
Construction of Adaptive Redundant Multiwavelet Packet and Its Application to Compound Faults Detection of Rotating Machinery
,”
Sci. China Technol. Sci.
,
55
(
8
), pp.
2083
2090
.10.1007/s11431-012-4846-1
6.
Yu
,
D. J.
,
Cheng
,
J. S.
, and
Yang
,
Y.
,
2005
, “
Application of EMD Method and Hilbert Spectrum to the Fault Diagnosis of Roller Bearings
,”
Mech. Syst. Signal Process.
,
19
(
2
), pp.
259
270
.10.1016/S0888-3270(03)00099-2
7.
Cheng
,
J. S.
,
Yu
,
D. J.
, and
Yang
,
Y.
,
2007
, “
The Application of Energy Operator Demodulation Approach Based on EMD in Machinery Fault Diagnosis
,”
Mech. Syst. Signal Process.
,
21
(
2
), pp.
668
677
.10.1016/j.ymssp.2005.09.005
8.
Cheng
,
J. S.
,
Yang
,
Y.
, and
Yu
,
D. J.
,
2012
, “
A Rotating Machinery Fault Diagnosis Method Based on Local Mean Decomposition
,”
Digital Signal Process.
,
22
(
2
), pp.
356
366
.10.1016/j.dsp.2011.09.008
You do not currently have access to this content.