Deep Transfer Learning Framework for Bearing Fault Detection in Motors

The domain of fault detection has seen tremendous growth in recent years. Because the growing demand for uninterrupted operations different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component motor. PHM bearing crucial operation. Conventional artificial intelligence techniques require feature extraction selection detection. This process often restricts performance such approaches. Deep learning enables autonomous selection. Given advantages deep learning, article presents transfer learning–based method pretrained ResNetV2 model used as base develop effective strategy faults. faults, including outer race fault, inner ball defect, included developing model. necessity manual been reduced by proposed method. Additionally, straightforward 1D 2D data conversion suggested, altogether eliminating requirement Different metrics estimated confirm efficacy strategy, results show that technique effectively detected