Neural-network-based motor rolling bearing fault diagnosis
Li, B.
Chow, M.-Y.
Tipsuwan, Y.
Hung, J.C.
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Oct 2000
Volume: 47,
Issue: 5
On page(s): 1060-1069
ISSN: 0278-0046
References Cited: 28
CODEN: ITIED6
INSPEC Accession Number: 6729435
Digital Object Identifier: 10.1109/41.873214
Current Version Published: 2002-08-06
Abstract
Motor systems are very important in modern society. They convert
almost 60% of the electricity produced in the US into other forms of
energy to provide power to other equipment. In the performance of all
motor systems, bearings play an important role. Many problems arising in
motor operations are linked to bearing faults. In many cases, the
accuracy of the instruments and devices used to monitor and control the
motor system is highly dependent on the dynamic performance of the motor
bearings. Thus, fault diagnosis of a motor system is inseparably related
to the diagnosis of the bearing assembly. In this paper, bearing
vibration frequency features are discussed for motor bearing fault
diagnosis. This paper then presents an approach for motor rolling
bearing fault diagnosis using neural networks and time/frequency-domain
bearing vibration analysis. Vibration simulation is used to assist in
the design of various motor rolling bearing fault diagnosis strategies.
Both simulation and real-world testing results obtained indicate that
neural networks can be effective agents in the diagnosis of various
motor bearing faults through the measurement and interpretation of motor
bearing vibration signatures
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