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Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Witold Glowacz Zygfryd Glowacz
Archives of Electrical Engineering | 2015 | vol. 64 | No 1 March | 29-35 | DOI: 10.1515/aee-2015-0004
Keywords DC motor diagnostic investigations FFT Bayes classifier
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Abstract

In this article results of diagnostic investigations of separately excited DC motor were presented. In diagnostics were applied a Fourier analysis method based on the fast Fourier transform (FFT) and a recognition method using Bayes classifier. In training process a set of the most important frequencies has been determined for which differences of corresponding signals in two states are the largest. Three categories of signals have been recognized in identification process: faultless state, state of the rotor broken one coil and state of the rotor shorted three coils

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Authors and Affiliations

Witold Glowacz
Zygfryd Glowacz

Fault diagnosis of electrical faults of three-phasei nduction motors using acoustic analysis

Adam Glowacz Maciej Sulowicz Jarosław Kozik Krzysztof Piech Witold Glowacz Zhixiong Li Frantisek Brumercik Miroslav Gutten Daniel Korenciak Anil Kumar Guilherme Beraldi Lucas Muhammad Irfan Wahyu Caesarendra Hui Lui
Bulletin of the Polish Academy of Sciences Technical Sciences | 2024 | 72 | 1 | e148440 | DOI: 10.24425/bpasts.2024.148440
Keywords acoustic signal induction motor fault neural network
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Abstract

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.
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Authors and Affiliations

Adam Glowacz
1
ORCID: ORCID
Maciej Sulowicz
1
ORCID: ORCID
Jarosław Kozik
2
ORCID: ORCID
Krzysztof Piech
2
ORCID: ORCID
Witold Glowacz
3
ORCID: ORCID
Zhixiong Li
4 5
ORCID: ORCID
Frantisek Brumercik
6
ORCID: ORCID
Miroslav Gutten
7
ORCID: ORCID
Daniel Korenciak
7
Anil Kumar
8
ORCID: ORCID
Guilherme Beraldi Lucas
9
ORCID: ORCID
Muhammad Irfan
10
ORCID: ORCID
Wahyu Caesarendra
4 11
ORCID: ORCID
Hui Lui
12
ORCID: ORCID
  1. Cracow University of Technology, Faculty of Electrical and Computer Engineering, Department of Electrical Engineering, ul. Warszawska 24,31-155 Kraków, Poland
  2. AGH University of Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of PowerElectronics and Energy Control Systems, al. A. Mickiewicza 30, 30-059 Kraków, Poland
  3. AGH University of Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of AutomaticControl and Robotics, al. A. Mickiewicza 30, 30-059 Krakw, Poland
  4. Faculty of Mechanical Engineering, Opole University of Technology, Opole 45-758, Poland
  5. University of Religions and Denomina, Qom, Iran
  6. University of Zilina, Faculty of Mechanical Engineering, Department of Design and Machine Elements, Univerzitna 1, 010 26 Zilina, Slovakia
  7. University of Zilina, Faculty of Electrical Engineering and Information Technology, 8215/1 Univerzitna, 01026 Zilina, Slovakia
  8. Wenzhou University, College of Mechanical and Electrical Engineering, Wenzhou, 325 035, China
  9. Sao Paulo State University, Department of Electrical Engineering, Av. Eng. Luís Edmundo Carrijo Coube, 14-01, Bauru, Sao Paulo, Brazil
  10. Najran University Saudi Arabia, Electrical Engineering Department, College of Engineering, Najran 61441, Saudi Arabia
  11. Faculty of Integrated Technologies, Universiti Brunei Darusalam, Jalan Tungku Link, Gadong BE1410, Brunei
  12. China Jiliang University, College of Quality and Safety Engineering, Hangzhou 310018, China

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