Detection of Deterioration of Three-phase Induction Motor using Vibration Signals-Reference-Cited by-同舟云学术

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

Published:2019-11-21 Issue:6 Volume:19 Page:241-249
ISSN:1335-8871
Container-title:Measurement Science Review
language:en
Short-container-title:

Author:

Glowacz Adam¹,Glowacz Witold¹,Kozik Jarosław²,Piech Krzysztof²,Gutten Miroslav³,Caesarendra Wahyu⁴,Liu Hui⁵,Brumercik Frantisek⁶,Irfan Muhammad⁷,Faizal Khan Z.⁸

Affiliation:

1. AGH University of Science and Technology , Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics , Al. A. Mickiewicza 30, 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 , Al. A. Mickiewicza 30, 30-059 Kraków , Poland

3. University of Zilina , Faculty of Electrical Engineering , 1, Univerzitna Str., 01026 Zilina , Slovakia

4. Faculty of Integrated Technologies , Universiti Brunei Darussalam , Jalan Tungku Link, Gadong BE1410 , Brunei Darussalam

5. College of Quality and Safety Engineering , China Jiliang University , Hangzhou 310018 , China

6. University of Zilina , Mechanical Engineering Faculty, Department of Desing and Machine Elements , 1 Univerzitna Str., 01026 Zilina , Slovakia

7. Najran University , Electrical Engineering Department , Kingdom of Saudi Arabia

8. Shaqra University , College of Computing and Information Technology, Department of Computer Science , Kingdom of Saudi Arabia

Abstract

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 %.

Publisher

Walter de Gruyter GmbH

Subject

Instrumentation,Biomedical Engineering,Control and Systems Engineering

Link

https://www.sciendo.com/pdf/10.2478/msr-2019-0031

Reference36 articles.

1. [1] Tulicki, J., Petryna, J., Sulowicz, M. (2016). Fault diagnosis of induction motors in selected working conditions based on axial flux signals. Technical Trasactions, Electrical Engineering Issue 3-E, 99-113. DOI: 10.4467/2353737XCT.16.269.6068.10.4467/2353737XCT.16.269.6068

2. [2] Ewert, P. (2017). Use of axial flux in the detection of electrical faults in induction motors. In International Symposium on Electrical Machines (SME). IEEE, DOI: 10.1109/ISEM.2017.7993571.10.1109/ISEM.2017.7993571

3. [3] Pietrowski, W. (2011). Application of Radial Basis Neural Network to diagnostics of induction motor stator faults using axial flux. Przeglad Elektrotechniczny, 87 (6), 190-192.

4. [4] Fulnecek, J., Misak, S. (2018). Stator current and axial magnetic flux analysis of induction motor. In International Conference on Diagnostics in Electrical Engineering (Diagnostika). IEEE, DOI: 10.1109/DIAGNOSTIKA.2018.8526025.10.1109/DIAGNOSTIKA.2018.8526025

5. [5] Calis, H. (2014). Vibration and motor current analysis of induction motors to diagnose mechanical faults. Journal of Measurements in Engineering, 2 (4), 190-198.

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