Run-to-failure vibration dataset of self-aligning double-row ball bearings - PART 2

Data sourced from actual operational systems constitutes a pivotal asset for scholarly investigations in the domain of machine diagnostics and prognostics. The exigency for such data has witnessed a notable surge in recent times, primarily propelled by the burgeoning interest in prognostic methodologies and the advancement of artificial intelligence (AI) technologies tailored for predictive maintenance applications. When harnessed for fault detection and prognostic endeavors, data must inherently possess the capacity to furnish insights into the degradation phenomena inherent to machinery. Moreover, a fundamental aim of prognostics entails the anticipation of the remaining useful life (RUL), a task necessitating substantial datasets for the application of data-driven techniques or the validation of physics-based models. Bearings, being subjected to a diverse spectrum of loads and fatigue stresses, carry the potential for catastrophic failure, thereby impacting the operational integrity of entire machinery systems or industrial plants. The Department of Engineering at the University of Ferrara has undertaken an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the lifecycle of self-aligning double row rolling element bearings. Six accelerated run-to-failure trials were conducted, during which acceleration signals were continuously captured utilizing a uniaxial accelerometer. Concurrently, a radial load was imposed on the bearing housing and regulated via a load cell. Ensuring consistency, the shaft speed was maintained at a constant level, facilitated by an electric motor actuated by an inverter. The resultant dataset encapsulates acceleration signals along the radial axis spanning the entirety of the experimental tests, thereby offering a valuable resource for both scholarly inquiry and industrial applications alike. This dataset provides the Part 2 of data collected during the experimental campaign. Data is provided in .mat format and each file contains two variables: the acceleration signal in radial direction and the sampling frequency.