vvanggeng / TSC-CNN
基于一维卷积神经网络(1D-CNN)的多元时间序列分类
☆71Updated 4 years ago
Related projects ⓘ
Alternatives and complementary repositories for TSC-CNN
- 使用改良的Transformer模型应用于多维时间序列的分类任务上☆20Updated 3 years ago
- 由于CSDN博客里面不能直接上代码链接,涉嫌营销推广,因此建一个github仓库用于整理这些代码链接☆152Updated last year
- 轴承故障检测 训练赛第30名代码☆123Updated 5 years ago
- [深度应用]·DC竞赛轴承故障检测开源Baseline(基于Keras1D卷积 val_acc:0.99780)☆179Updated 5 years ago
- 一维卷积神经网络☆36Updated 5 years ago
- Encoding time series as images using GAF operation by pyts.☆212Updated 2 years ago
- 1DCNN Fault Detection(1DCNN的轴承故障诊断)☆132Updated 2 years ago
- CSDN中的代码 在github中建立仓库存储☆26Updated 2 years ago
- 毕设研究课题:根据轴承的振动序列数据来诊断轴承故障。☆120Updated 3 years ago
- This is a case of bearing fault intelligent diagnosis. The program is written in MATLAB. The main techniques used are feature detection a…☆49Updated 3 years ago
- ☆94Updated 5 years ago
- A python program to build ResNet-1D model and DRSN-1D model in keras environment.☆11Updated last year
- 基于无监督和迁移学习的旋转机械故障诊断☆33Updated 4 years ago
- EEMD、LSTM、time series prediction、DO、Deep Learning☆84Updated 3 years ago
- for de-noising☆30Updated 7 years ago
- 故障诊断方面的论文阅读☆16Updated 5 years ago
- 基于KNN和DTW的时间序列分类☆19Updated 4 years ago
- 轴承有3种故障:外圈故障,内圈故障,滚珠故障,外加正常的工作状态。如表1所示,结合轴承的3种直径(直径1,直径2,直径3),轴承的工作状态有10类☆31Updated 5 years ago
- with LSTM method to solve bearing fault diagnosis classification☆62Updated 7 years ago
- One model for RUL and fault prognostic prediction on XJTU bearing dataset☆88Updated 5 years ago
- ☆92Updated 2 years ago
- Siamese network for bearing fault diagnosis☆84Updated 4 years ago
- ☆193Updated 5 years ago
- The code of Understanding and Learning Discriminant Features based on Multi-Attention 1DCNN for Wheelset Bearing Fault Diagnosis.☆24Updated 5 years ago
- 利用西储大学开源的轴承故障数据,开发简单的人工神经网络,以实现对轴承故障的检测及识别。☆45Updated 3 years ago
- 基于深度学习的滚动轴承故障诊断方法☆170Updated 5 years ago
- Using LSTM to predict Remaining Useful Life of CMAPSS Dataset☆84Updated 6 years ago
- 1D CNN for CWRU rolling bearings dataset☆37Updated 6 years ago
- ☆33Updated 4 years ago
- ☆59Updated 5 years ago