Run-to-failure vibration dataset of self-aligning double-row ball bearings - PART 1

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 1 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.

源自实际运行系统的数据，乃是机械诊断与预后领域学术研究的核心资产。近年来，此类数据的迫切需求显著增长，这主要源于预后方法学的日益受关注，以及专为预测性维护应用开发的人工智能（AI）技术的进步。当用于故障检测与预后工作时，数据本身必须能够揭示机械固有的退化现象。此外，预后的核心目标在于预测剩余使用寿命（Remaining Useful Life, RUL），而该任务需要借助大规模数据集来实现数据驱动技术的应用，或是对基于物理的模型进行验证。

轴承承受着多样的载荷与疲劳应力，存在发生灾难性失效的可能，进而影响整个机械系统或工业厂区的运行可靠性。

费拉拉大学工程系开展了一项大规模实验研究，旨在记录调心双列滚动轴承全生命周期内振动信号的时序演化过程。本次实验共开展六次加速跑合至失效的试验，试验期间通过单轴加速度计持续采集加速度信号。同时，通过测力传感器对轴承座施加并调控径向载荷；借助变频器驱动的电动机，将轴转速维持在恒定水平，以保证实验一致性。

本次生成的数据集涵盖了所有实验测试中沿径向轴采集的加速度信号，可为学术研究与工业应用提供极具价值的资源。

本数据集为本次实验研究中采集的第一部分数据。

数据以.mat格式提供，每个文件包含两个变量：径向加速度信号与采样频率。