Near real-time skidding rate estimation in rolling bearings using Fiber Bragg Grating sensors

Rolling bearings are critical components in rotating machinery, directly influencing system stability, reliability, and safety. Even in healthy bearings, improper load or dynamic effects can cause roller–raceway slip, which, if sustained, leads to skidding and increases frictional heating and wear at the raceway contacts. Skidding occurs when the tangential speed of the rollers falls below the theoretical value required for pure rolling based on the inner race speed, while over-skidding arises when it exceeds this value. Accurately determining skidding and over-skidding rates requires precise estimation of both the theoretical and actual ball pass frequency of the outer race (BPFO). Existing experimental approaches for skidding estimation, including vibration, proximity, laser and strain-based sensing methods, are often limited by low sensitivity to localized roller–raceway interactions, installation constraints, or susceptibility to electrical noise. By leveraging localized roller–raceway strain, this study introduces a direct methodology to track the actual BPFO, providing deeper insights into near real-time roller skidding estimation under different working conditions. To enable this capability, a near real-time skidding-rate estimation method is developed using FBG sensing and finite-block signal processing with bounded latency. Practical implementation is enabled by an optimized FBG sensing configuration at the load-carrying region of the outer race, allowing reliable capture of localized roller-pass strain impulses. Experimental results under various load and speed conditions demonstrate that the proposed method can accurately detect and quantify both skidding and over-skidding in near real-time. The measured trends align with bearing catalog guidance: severe skidding occurs at low loads, while sufficient radial loading effectively suppresses skidding.