Vibration-based Detection and Classification of Mechanical Defects in Induction Motor-driven Systems during the Starting Transient

Battulga B., Shaikh M. F., GÖKTAŞ T., ARKAN M., Lee S. B.

IEEE Transactions on Industry Applications, cilt.61, sa.5, ss.6994-7003, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 61 Sayı: 5
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1109/tia.2025.3554469
  • Dergi Adı: IEEE Transactions on Industry Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.6994-7003
  • Anahtar Kelimeler: Accelerometer, fault diagnosis, imbalance, looseness, misalignment, piezoelectric transducer, short time fourier transform, spectral analysis, starting transient, time-frequency transformation, velocity, vibration analysis
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Vibration analysis is considered the most common and effective means of detecting mechanical faults such as imbalance, misalignment, and looseness in induction motor driven systems. Most mechanical problems result in an increase in vibration at multiples of the rotor speed frequency (1x) making it difficult to discern the source of vibration. In case of a fault alarm, the maintenance engineer usually performs a walk-around test to identify the source of vibration for planning maintenance, and therefore, is exposed to safety risks. In this paper, a new remote and automated test method for identifying the source of mechanical vibration during the starting transient of induction motors is proposed. The level and speed-dependency of vibration during rotor acceleration are used for identifying imbalance from other mechanical defects that produce 1x vibration. Test results on a 380 V, 5.5 kW induction motor under mechanical defects are given for verification. It is shown that the proposed method can provide automated identification of the source of vibration enabling maintenance to be performed in a safe, low cost, and efficient manner. The data acquired and analyzed for the testing are described and shared through this paper.