Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Adam Glowacz 1 , Witold Glowacz 1 , Jarosław Kozik 2 , Krzysztof Piech 2 , Miroslav Gutten 3 , Wahyu Caesarendra 4 , Hui Liu 5 , Frantisek Brumercik 6 , Muhammad Irfan 7  and Z. Faizal Khan 8
  • 1 AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics, 30-059, Kraków, Poland
  • 2 AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Power Electronics and Energy Control Systems, 30-059, Kraków, Poland
  • 3 University of Zilina, Faculty of Electrical Engineering, 01026, Zilina, Slovakia
  • 4 Faculty of Integrated Technologies, Universiti Brunei Darussalam, BE1410, Gadong, Brunei Darussalam
  • 5 College of Quality and Safety Engineering, China Jiliang University, 310018, Hangzhou, China
  • 6 University of Zilina, Mechanical Engineering Faculty, Department of Desing and Machine Elements, 01026, Zilina, Slovakia
  • 7 Najran University, Electrical Engineering Department, Saudi Arabia
  • 8 Shaqra University, College of Computing and Information Technology, Department of Computer Science, Saudi Arabia


Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

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