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Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor

2022; Multidisciplinary Digital Publishing Institute; Volume: 12; Issue: 11 Linguagem: Inglês

10.3390/app12115513

2076-3417

Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Yukio Mizuno, Hisahide Nakamura,

Engineering Diagnostics and Reliability

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates.