TF-C Pretrain FD-B

- 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: FD-A and FD-B are subsets taken from the FD 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 (FD-A) and Faulty Detection Condition B (FD-B) , 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间序列自监督对比预训练（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（FD dataset）的子集，该数据集采集自用于监测滚动轴承（rolling bearings）状态并检测其损伤的机电驱动系统。本次共采集了四种不同工况下的数据集子集，其包含的参数有转速、负载转矩与径向力。每一个滚动轴承可分为无损伤、内圈损伤（inner damaged）与外圈损伤（outer damaged）三种状态，对应总计三类样本。我们将对应工况A与工况B的子集分别命名为故障检测工况A（Faulty Detection Condition A, FD-A）与故障检测工况B（Faulty Detection Condition B, FD-B）。每条原始记录为单通道数据，采样频率为64千赫兹，单条记录时长为4秒。为处理长时长数据，我们遵循Eldele等人提出的处理流程：采用长度为5120个观测值的滑动窗口（sliding window），滑动步长（shifting length）设为1024或4096，以使得最终各类样本的数量相对均衡。

搜集汇总

背景与挑战

背景概述

TF-C Pretrain FD-B是一个用于滚动轴承故障检测的数据集，包含三个类别，采样频率高，通过滑动窗口技术处理数据以平衡样本。数据集与一篇关于时间序列自监督对比预训练的论文相关，并提供了GitHub代码库链接。