xiaosongshine / bearing_detection_by_conv1d
[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)
☆184Updated 5 years ago
Alternatives and similar repositories for bearing_detection_by_conv1d:
Users that are interested in bearing_detection_by_conv1d are comparing it to the libraries listed below
- 轴承故障检测 训练赛第30名代码☆122Updated 5 years ago
- ☆136Updated 7 years ago
- 基于深度学习的滚动轴承故障诊断方法☆175Updated 5 years ago
- ☆198Updated 5 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。☆121Updated 3 years ago
- CNN for mechanical fault diagnosis☆292Updated 6 years ago
- ☆98Updated 5 years ago
- 基于深度学习机械设备故障诊断模型☆158Updated 7 years ago
- 1DCNN Fault Detection(1DCNN的轴承故障诊断)☆143Updated 2 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☆32Updated 5 years ago
- with LSTM method to solve bearing fault diagnosis classification☆64Updated 7 years ago
- 1D CNN for CWRU rolling bearings dataset☆38Updated 6 years ago
- wdcnn轴承故障模型☆350Updated 6 years ago
- 使用TensorFlow建立简单的轴承故障诊断模型☆97Updated 6 years ago
- ☆60Updated 5 years ago
- to prediction the remain useful life of bearing based on 2012 PHM data☆275Updated 4 years ago
- CNN applied to bearing signals for analysis☆89Updated 5 years ago
- ☆159Updated 3 years ago
- 故障诊断方面的论文阅读☆16Updated 5 years ago
- 基于无监督和迁移学习的旋转机械故障诊断☆33Updated 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。☆46Updated 4 years ago
- Using LSTM to predict Remaining Useful Life of CMAPSS Dataset☆85Updated 6 years ago
- One model for RUL and fault prognostic prediction on XJTU bearing dataset☆91Updated 5 years ago
- 基于注意力机制的少量样本故障诊断 pytorch☆207Updated last year
- 基于一维卷积神经网络(1D-CNN)的多元时间序列分类☆73Updated 4 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…☆50Updated 3 years ago
- TensorFlow implementation of a CNN based mechanical science paper☆45Updated 6 years ago
- Remaining Useful Life Prediction Using RNN/LSTM/GRU Neural Networks☆127Updated 3 years ago
- Deep learning in PHM,Deep learning in fault diagnosis,Deep learning in remaining useful life prediction☆424Updated 3 years ago
- ☆93Updated last year