Classification of Rolling Bearing Defects Based on the Direct Analysis of Phase Currents

Oliwia Frankiewicz, Maciej Skowron, Jeremi Jan Jarosz, Sebastien Weisse, Jerome Valire and Krzysztof Szabat ()
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Oliwia Frankiewicz: Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Maciej Skowron: Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Jeremi Jan Jarosz: Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Sebastien Weisse: SAFRAN Electrical & Power, Parc d’activité d’Andromède, 1, rue Louis Blériot, 31702 Blagnac, France
Jerome Valire: SAFRAN Electrical & Power, Parc d’activité d’Andromède, 1, rue Louis Blériot, 31702 Blagnac, France
Krzysztof Szabat: Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland

Energies, 2025, vol. 18, issue 10, 1-14

Abstract: Electric machines are gaining popularity in transport and replacing internal combustion engines. However, the diagnosis of their faults remains an ongoing problem. Traditional diagnostic methods, such as vibration, sound, and temperature analysis, have limitations in practical applications, particularly because of external interference and the need for additional sensors. This paper presents a new diagnostic approach based on convolutional neural networks (CNNs) and direct analysis of current signals. The proposed solution allows for a significant reduction in the number of samples required for effective diagnostics. The neural network, operating on 500 signal samples, achieved a classification efficiency of 99.85–100% for each category of damage investigated. Tests were conducted to determine the effect of noise on the accuracy of the system. This study compares applications based on mechanical vibration signals and the proposed algorithm based on phase current signals. The results indicate that the proposed approach can be successfully applied to real-world monitoring systems for electrical machinery, offering a high-efficiency diagnostic tool while fulfilling the limitations of demanding measurement systems.

Keywords: induction motor drive; fault diagnosis; rolling bearing faults; artificial intelligence; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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