Rights statement: ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Accepted author manuscript, 1.33 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Real-time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils
AU - Zhu, Q.
AU - Wang, X.
AU - Wang, H.
AU - Xia, M.
AU - Lu, S.
AU - Liu, B.
AU - Li, G.
AU - Cao, W.
N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2021/12/20
Y1 - 2021/12/20
N2 - The die cast rotor bars in squirrel cage induction motors (SCIMs) are easily subjected to porosity or other defects in production, which considerably affects the motors' reliability and efficiency in operation. Planar flux sensing coils have been investigated for the defect detection of SCIM rotor. However, these types of sensors cannot accurately evaluate the severity of porosity or broken bar. This study develops a novel instrument to inspect and quantitatively analyze the rotor quality of SCIM. The sensor consists of the electromagnetic flux sensing coils directly from a SCIM stator. By injecting a DC voltage at phases A and B of the sensor, the induced voltage signal is generated from phase C. A quantitative fault indicator (QFI) is constructed on the basis of the instrument voltage output. The variation trend of the QFI with respect to fault severity is investigated by establishing a theoretical sensor model. Experimental results indicate that the proposed method can accurately detect the porosity and broken bar and evaluate their severities for the die cast rotor. The developed solution can be easily implemented with low cost and computational complexity, which can achieve real-time inspection of SCIM rotor in the production line.
AB - The die cast rotor bars in squirrel cage induction motors (SCIMs) are easily subjected to porosity or other defects in production, which considerably affects the motors' reliability and efficiency in operation. Planar flux sensing coils have been investigated for the defect detection of SCIM rotor. However, these types of sensors cannot accurately evaluate the severity of porosity or broken bar. This study develops a novel instrument to inspect and quantitatively analyze the rotor quality of SCIM. The sensor consists of the electromagnetic flux sensing coils directly from a SCIM stator. By injecting a DC voltage at phases A and B of the sensor, the induced voltage signal is generated from phase C. A quantitative fault indicator (QFI) is constructed on the basis of the instrument voltage output. The variation trend of the QFI with respect to fault severity is investigated by establishing a theoretical sensor model. Experimental results indicate that the proposed method can accurately detect the porosity and broken bar and evaluate their severities for the die cast rotor. The developed solution can be easily implemented with low cost and computational complexity, which can achieve real-time inspection of SCIM rotor in the production line.
KW - Bars
KW - circular flux sensing coils
KW - fault diagnosis
KW - Induction motors
KW - QFI
KW - real-time edge computing
KW - rotor defect detection
KW - Rotors
KW - SCIM
KW - Sensors
KW - Stator windings
KW - Stators
KW - Voltage
KW - Defects
KW - Edge computing
KW - Failure analysis
KW - Fault detection
KW - Porosity
KW - Squirrel cage motors
KW - Circular flux sensing coil
KW - Defect detection
KW - Fault indicators
KW - Faults diagnosis
KW - Inductions motors
KW - Quantitative fault indicator
KW - Real- time
KW - Real-time edge computing
KW - Rotor defect detection
KW - Sensing coils
KW - Squirrel cage induction motor
KW - Stator winding
U2 - 10.1109/TII.2021.3136560
DO - 10.1109/TII.2021.3136560
M3 - Journal article
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
ER -