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

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.