Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 2

Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions. Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears. The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours. The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance. Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition. This dataset provides the Part 2 of data collected during the experimental campaign.源自真实运行工况的数据，是机械故障诊断与健康预后研究的关键资源，尤其在预测性维护以及人工智能（AI）方法应用日益普及的背景下。 针对故障检测与健康预后应用场景，数据集需捕捉退化现象随时间的演变过程，从而实现对机械健康状态的评估，以及其剩余使用寿命（Remaining Useful Life, RUL）的预测。 达成上述目标，需要在真实且可变的工况下采集长期高质量的全生命周期失效前运行数据。 行星齿轮箱（Planetary Gearboxes）被广泛应用于工业与汽车系统中，且需承受复杂载荷以及齿轮间的动态交互作用。 费拉拉大学工程系开展了一项大规模试验研究，旨在记录三级行星齿轮箱在全运行周期内振动信号的时域演变过程。该试验在一台可变速变矩的专用背对背试验台上进行。研究人员在齿轮箱壳体上安装了单轴加速度传感器，以每小时一次的频率采集径向振动信号。系统持续运行直至1006个运行小时后，第三级太阳轮发生完全机械失效。 本次生成的数据集完整记录了该齿轮箱直至失效的全运行历程，并全面呈现了其退化演进过程。该数据集是故障诊断、健康预后及预测性维护领域学术研究与工业应用的宝贵资源。 数据集以.mat格式提供，由于存储库大小限制，被分为三个独立部分，每部分包含总试验数据的约三分之一。每个文件均包含径向加速度信号、采样频率以及对应采集时刻的运行工况。 本数据集为本次试验研究中采集的第二部分数据。