A Comparative Study of Image Representation for Roller Bearing Fault Diagnosis Using Pretrained Networks

Roller bearings are critical components in many types of machinery, and their failure may cause significant downtime and maintenance costs. Fault diagnosis of roller bearings is thus crucial for detecting potential problems before they cause catastrophic failure and for planning maintenance and repair operations ahead of time. Early detection of roller bearing failures can help to minimize costly machine downtime and save maintenance costs. This study uses the help of deep learning models for roller bearing fault diagnosis, which can help to minimize machinery downtime and maintenance costs. The study utilizes 12 deep learning modules, and they were evaluated using various image generation methods such as vibration plot, radar plot, polar plot, Hilbert–Huang transforms, spectrogram, and scalogram. From the experimental findings, the ResNet18 model has achieved a 100.00% accuracy when the spectrogram image generation method was employed. The findings highlight the importance of selecting and optimizing deep learning models for a specific maintenance application and contribute valuable insights for researchers and practitioners in reliability engineering.