Real-time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils

Qingyun Zhu, Xiaoxian Wang, Hui Wang, Min Xia, Siliang Lu, Bingyou Liu, Guoli Li, Wenping Cao

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Early online date20 Dec 2021
Publication statusE-pub ahead of print - 20 Dec 2021

Bibliographical note

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Funding: This work was supported in part by the National
Natural Science Foundation of China under Grants 52075002, 51637001, and
52075001, and the Open Research Fund of Anhui Key Laboratory of Detection
Technology and Energy Saving Devices, Anhui Polytechnic University, under
grant DTESD2020A01


  • Bars
  • circular flux sensing coils
  • fault diagnosis
  • Induction motors
  • QFI
  • real-time edge computing
  • rotor defect detection
  • Rotors
  • SCIM
  • Sensors
  • Stator windings
  • Stators
  • Voltage


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