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Abstract

The article presents the results concerning the use of clustering methods to identify signals of acoustic emission (AE) generated by partial discharge (PD) in oil-paper insulation. The conducted testing featured qualitative analysis of the following clustering methods: single linkage, complete linkage, average linkage, centroid linkage and Ward linkage. The purpose of the analysis was to search the tested series of AE signal measurements, deriving from three various PD forms, for elements of grouping (clusters), which are most similar to one another and maximally different than in other groups in terms of a specific feature or adopted criteria. Then, the conducted clustering was used as a basis for attempting to assess the effectiveness of identification of particular PD forms that modelled exemplary defects of the power transformer’s oil-paper insulation system. The relevant analyses and simulations were conducted using the Matlab estimation environment and the clustering procedures available in it. The conducted tests featured analyses of the results of the series of measurements of acoustic emissions generated by the basic PD forms, which were obtained in laboratory conditions using spark gap systems that modelled the defects of the power transformer’s oil-paper insulation.
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Authors and Affiliations

Sebastian Borucki
Jacek Łuczak
Dariusz Zmarzły
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Abstract

Condition monitoring in a centrifugal pump is a significant field of study in industry. The acoustic method offers a robust approach to detect cavitations in different pumps. As a result, an acoustic-based technique is used in this experiment to predict cavitation. By using an acoustic technique, detailed information on outcomes can be obtained for cavitation detection under a variety of conditions. In addition, various features are used in this work to analyze signals in the time domain using the acoustic technique. A signal in the frequency domain is also investigated using the fast Fourier method. This method has shown to be an effective tool for predicting future events. In addition, this experimental investigation attempts to establish a good correlation between noise characteristics and cavitation detection in a pump by using an acoustic approach. Likewise, it aims to find a good method for estimating cavitation levels in a pump based on comparing and evaluating different systems.
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Authors and Affiliations

Ahmed Ramadhan Al-Obaidi
1
ORCID: ORCID

  1. Faculty of Engineering, Department of Mechanical Engineering, Mustansiriyah University, Baghdad, Iraq
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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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Abstract

The paper presents the results of the application of the hierarchical clustering methods for the classification of the acoustic emission (AE) signals generated by eight basic forms of partial discharges (PD), which can occur in paper-oil insulation of power transformers. Based on the registered AE signals from the particular PD forms, using a frequency descriptor in the form of the power spectral density (PSD) of the signal, their representation in the form of the set of points on plane XY was created. Next, these sets were subjected to analysis using research algorithms consisting of selected clustering methods. Based on the suggested numeric performance indicators, the analysis of the degree of reproduction of the actual distribution of points showing the particular time waveforms of the AE signals from eight adopted PD forms (PD classes) in the obtained clusters was carried out. As a result of the analyses carried out, the clustering algorithms of the highest effectiveness in the identification of all eight PD classes, classified simultaneously, where indicated. Within the research carried out, an attempt to draw general conclusions as to the selection of the most effective hierarchical clustering method studied and the similarity function to be used for classification of the selected basic PD forms.
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Bibliography

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Authors and Affiliations

Sebastian Borucki
1
Jacek Łuczak
1
Marcin Lorenc
1

  1. Opole University of Technology, Opole, Poland

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