Multi-Axis Accelerometer Based Evaluation and Classification of Misalignment, Soft Foot, and Looseness Faults in Induction Motors

AYAS G. N., GÖKTAŞ T., ARKAN M., Lee S. B.

IEEE Sensors Journal, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1109/jsen.2026.3678373
  • Dergi Adı: IEEE Sensors Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Anahtar Kelimeler: Fault detection, induction motor (IM), looseness fault (Type B), parallel misalignment fault, soft foot fault, vibration analysis
  • İnönü Üniversitesi Adresli: Evet

Özet

In induction motors, possible faults can affect the dynamic performance of the motor and reduce the overall efficiency of the motor-driven systems. Therefore, it is crucial to detect potential faults early and accurately. In this study, parallel misalignment, soft foot, and looseness- Bolt (Type-B) faults have been investigated using multi-axis accelerometer data. For this purpose, a dedicated test setup is built to introduce different mechanical faults and analyze the frequency spectra of 3-axis vibration signals under varying torque profiles. Furthermore, a Random Forest–based artificial intelligence model has been developed to achieve highly accurate fault classification. Experimental results show that characteristic fault-related components in the vibration spectra such as fr (1X), 2fr (2X), 3fr (3X), and their sidebands (2fr+2sfs) and (3fr+2sfs) provide accurate and reliable fault detection. Moreover, the developed artificial intelligence model achieves fault classification with an accuracy ranging from 85.3% to 91.8%.