TF-C Pretrain FD-A
收藏资源简介：
- Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency - Paper link: - Github repo: https://github.com/mims-harvard/TFC-pretraining - Project website: <br> <strong>FD-A</strong> and <strong>FD-B</strong> are subsets taken from the <strong>FD</strong> dataset, which is gathered from an electromechanical drive system that monitors the condition of rolling bearings and detect damages in them. There are four subsets of data collected under various conditions, whose parameters include rotational speed, load torque, and radial force. Each rolling bearing can be undamaged, inner damaged, and outer damaged, which leads to three classes in total. We denote the subsets corresponding to condition A and condition B as Faulty Detection Condition A (<strong>FD-A</strong>) and Faulty Detection Condition B (<strong>FD-B</strong>) , respectively. Each original recording has a single channel with sampling frequency of 64k Hz and lasts 4 seconds. To deal with the long duration, we followe the procedure described by Eldele et al., that is, we use sliding window length of 5,120 observations and a shifting length of either 1,024 or 4,096 to make the final number of samples relatively balanced between classes.
论文：《基于时频一致性的时序自监督对比预训练》（原英文标题：Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency），论文链接：无，GitHub仓库：https://github.com/mims-harvard/TFC-pretraining，项目网站：无。 **FD-A**与**FD-B**为**FD**数据集的两个子集，该数据集采集自一套用于监测滚动轴承运行状态并检测其损伤缺陷的机电驱动系统。该数据集包含四组在不同工况下采集的子集，采集参数涵盖转速（rotational speed）、负载转矩（load torque）与径向力（radial force）。滚动轴承的健康状态可分为无损伤、内圈损伤与外圈损伤三种类型，因此该数据集共包含三个类别。我们将对应工况A与工况B的子集分别记为故障检测工况A（Faulty Detection Condition A，即**FD-A**）与故障检测工况B（Faulty Detection Condition B，即**FD-B**）。每条原始记录为单通道数据，采样频率为64千赫兹（64 kHz），单条时长为4秒。为处理较长的原始数据时长，我们遵循Eldele等人提出的处理流程：采用长度为5120个采样点的滑动窗口（sliding window），并将窗口移位长度（shifting length）设置为1024或4096，以使最终生成的各类样本数量相对均衡。
