Study on Fatigue Crack Propagation Caused by Sensor Slots in Intelligent Tapered Bearings

Electric-shovel top sheave bearings with sensor-embedded slots operate under harsh service loads, making them prone to fatigue crack initiation and propagation. Accurate predictions of crack growth within the bearing body are therefore essential for intelligent bearing design and reliability assessments because the bearing integrity directly affects shovel service life and safety. This paper presents a sub-modeling-based method that embeds initial cracks while preserving actual roller-ring boundary conditions and ensuring computational efficiency via adaptive mesh refinement. A global model first identifies critical crack-prone zones, after which the sub-model systematically examines the effects of the initial crack angle and sensor-embedded slot depth on the propagation behavior. The results indicate that both factors significantly increased the stress intensity factor (SIF). Among the evaluated designs, the 15 mm -deep slot produced the highest SIFs and the shortest predicted crack-propagation life, indicating that slot depth was a key design parameter under the investigated conditions. The findings provide theoretical support for the structural design and fatigue evaluation of intelligent electric-shovel top sheave bearings.