DocumentCode :
1390829
Title :
Neural-network-based motor rolling bearing fault diagnosis
Author :
Li, Bo ; Chow, Mo-Yuen ; Tipsuwan, Yodyium ; Hung, James C.
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
Volume :
47
Issue :
5
fYear :
2000
fDate :
10/1/2000 12:00:00 AM
Firstpage :
1060
Lastpage :
1069
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
Keywords :
electric machine analysis computing; electric motors; fault diagnosis; frequency-domain analysis; machine bearings; neural nets; time-domain analysis; vibrations; bearing vibration frequency; frequency-domain bearing vibration analysis; motor bearing fault diagnosis; motor rolling bearing fault diagnosis; neural-network; time-domain bearing vibration analysis; vibration simulation; Assembly systems; Control systems; Energy conversion; Fault diagnosis; Frequency; Instruments; Monitoring; Neural networks; Rolling bearings; Vibration measurement;
fLanguage :
English
Journal_Title :
Industrial Electronics, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0046
Type :
jour
DOI :
10.1109/41.873214
Filename :
873214
Link To Document :
بازگشت