TF-C Pretrain FD-A

- 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.

搜集汇总

背景与挑战

背景概述

TF-C Pretrain FD-A是一个用于滚动轴承故障检测的预处理数据集，属于机电驱动系统故障检测领域。数据集包含三个类别（无损坏、内部损坏、外部损坏），总大小约532.92 MB，包含训练、验证和测试三个文件。数据采集自特定条件A，采样频率为64kHz，采用滑动窗口处理以平衡类别样本数。