<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 %.</p></abstract>

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 %.

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

Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics

Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Power Electronics and Energy Control Systems

College of Quality and Safety Engineering

Mechanical Engineering Faculty, Department of Desing and Machine Elements

College of Computing and Information Technology, Department of Computer Science

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor....

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

Online veröffentlicht: 21. Nov. 2019

Seitenbereich: 241 - 249

Eingereicht: 08. Juni 2019

Akzeptiert: 21. Okt. 2019

DOI: https://doi.org/10.2478/msr-2019-0031

Schlüsselwörter
signal processing, vibration signal, induction motor, deterioration, diagnosis

© 2019 Adam Glowacz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 21. Nov. 2019

Seitenbereich: 241 - 249

Eingereicht: 08. Juni 2019

Akzeptiert: 21. Okt. 2019

DOI: https://doi.org/10.2478/msr-2019-0031

Schlüsselwörtersignal processing, vibration signal, induction motor, deterioration, diagnosis

© 2019 Adam Glowacz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
signal processing, vibration signal, induction motor, deterioration, diagnosis