TY - GEN
T1 - Health Monitoring of Three-Phase Induction Motor Using Current and Vibration Signature Analysis
AU - Moiz, Muhammad Sarfraz
AU - Shamim, Shazaib
AU - Abdullah, Muhammad
AU - Khan, Hamdan
AU - Hussain, Imtiaz
AU - Iftikhar, Anas Bin
AU - Memon, T. D.
N1 - Funding Information:
ACKNOWLEDGMENT This research work is carried out at DHA Suffa University and is supported by HEC National Center for Robotics and Automation joint Lab titled "Haptics, Human Robotics and Condition Monitoring Lab" established at Mehran UET, Jamshoro, and NED UET Karachi.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - This paper revolves around the discussion of detection of faults occurs in three-phase induction motor especially outer-race bearing faults using two significant methods, Current Signature Analysis and Vibration Signature Analysis, in order to deploy predictive maintenance technique. Detection of bearing fault is important because most of the failures in induction motors are related to bearing faults which can lead to excessive downtimes and large revenue losses. Early detection of fault helps to reduce downtime and unexpected breakdowns. The current signature analysis uses stator currents spectrum to determine fault harmonics around the fundamental frequency. Every machine generates vibration and due to dynamic stresses, vibrational behavior of the machine is influenced by the mechanical condition. This disturbance can be analyzed by Vibration signature analysis. The experiment is done on induction motor of 1 HP, connected to three-phase 50 Hz supply and 6303 model bearings are artificially damaged to generate fault conditions. From experimental results, we have compared the above two techniques for fault detection and analyze the bearing fault frequency.
AB - This paper revolves around the discussion of detection of faults occurs in three-phase induction motor especially outer-race bearing faults using two significant methods, Current Signature Analysis and Vibration Signature Analysis, in order to deploy predictive maintenance technique. Detection of bearing fault is important because most of the failures in induction motors are related to bearing faults which can lead to excessive downtimes and large revenue losses. Early detection of fault helps to reduce downtime and unexpected breakdowns. The current signature analysis uses stator currents spectrum to determine fault harmonics around the fundamental frequency. Every machine generates vibration and due to dynamic stresses, vibrational behavior of the machine is influenced by the mechanical condition. This disturbance can be analyzed by Vibration signature analysis. The experiment is done on induction motor of 1 HP, connected to three-phase 50 Hz supply and 6303 model bearings are artificially damaged to generate fault conditions. From experimental results, we have compared the above two techniques for fault detection and analyze the bearing fault frequency.
KW - Current signature analysis
KW - preventive maintenance
KW - spectral analysis
KW - stator current
KW - Vibration signature analysis
UR - http://www.scopus.com/inward/record.url?scp=85079246418&partnerID=8YFLogxK
U2 - 10.1109/ICRAI47710.2019.8967356
DO - 10.1109/ICRAI47710.2019.8967356
M3 - Conference contribution
AN - SCOPUS:85079246418
T3 - 2019 International Conference on Robotics and Automation in Industry, ICRAI 2019
BT - 2019 International Conference on Robotics and Automation in Industry, ICRAI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Robotics and Automation in Industry, ICRAI 2019
Y2 - 21 October 2019 through 22 October 2019
ER -