Multi-Axis Accelerometer Based Evaluation and Classification of Misalignment, Soft Foot, and Looseness Faults in Induction Motors
IEEE Sensors Journal, cilt.26, sa.9, ss.14106-14116, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 9
- 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
- Sayfa Sayıları: ss.14106-14116
- 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%.