Mudestreda (Mudestreda Multimodal Device State Recognition Dataset)
Mudestreda Multimodal Device State Recognition Dataset
obtained from real industrial milling device with Time Series and Image Data for Classification, Regression, Anomaly Detection, Remaining Useful Life (RUL) estimation, Signal Drift measurement, Zero Shot Flank Took Wear, and Feature Engineering purposes.
The official dataset used in the paper "Multimodal Isotropic Neural Architecture with Patch Embedding" ICONIP23.
Official Minape repository: https://github.com/hubtru/Minape
Official Mudestreda dataset: https://zenodo.org/records/8238653
Conference paper: https://link.springer.com/chapter/10.1007/978-981-99-8079-6_14
Mudestreda (MD) | Size 512 Samples (Instances, Observations)| Modalities 4 | Classes 3 |
Future research: Regression, Remaining Useful Life (RUL) estimation, Signal Drift detection, Anomaly Detection, Multivariate Time Series Prediction, and Feature Engineering.
Overview
- Task: Uni/Multi-Modal Classification
- Domain: Industrial Flank Tool Wear of the Milling Machine
- Input (sample): 4 Images: 1 Tool Image, 3 Spectrograms (X, Y, Z axis)
- Output: Machine state classes:
Sharp,Used,Dulled - Evaluation: Accuracies, Precision, Recal, F1-score, ROC curve
- Each tool's wear is categorized sequentially: Sharp → Used → Dulled.
- The dataset includes measurements from ten tools: T1 to T10.
- Data splitting options include random or chronological distribution, without shuffling.
- Options:
- Original data or Augmented data
- Random distribution or Tool Distribution
Use Cases:
| Input | Model | Output |
|---|---|---|
| Use Cases: | ||
| 4 Images (1 Tool Image, 3 Spectrograms (X, Y, Z)) | Classification Model | Class (Flank Tool Wear: Sharp, Used, Dulled) |
| 3 Spectrograms (X,Y,Z axis) | Classification Model | Class (Flank Tool Wear) |
| 1 Tool Image | Classification Model | Image Class (Flank Tool Wear) |
| Future Work: | ||
| [1, ..., 4] Images | Model | Remaining Useful Life (RUL) estimation |
| [1, ..., 4] Images | Monitoring Model | Fault and Anomaly Detection |
| [1, ..., 4] Images | Forecasting Model | Multivariate Time Series Prediction |
| [1, ..., 3] Spectrograms | Model | Signal Drift measurement |
| [1, ..., 4] Images | Regression Model | Zero-Shot Flank Tool Wear (in µm, 10e-6 meter) |
| [1, ..., 4] Images | Feature Engineering | Diagnostic Feature Designer |
If you use Mudestreda dataset cite the work Minape @ ICONIP2023.
Cite the Paper:
If you reference the papr or you use Mudestreda dataset cite the work Minape @ ICONIP2023
@inproceedings{truchan2023multimodal,
title={Multimodal Isotropic Neural Architecture with Patch Embedding},
author={Truchan, Hubert and Naumov, Evgenii and Abedin, Rezaul and Palmer, Gregory and Ahmadi, Zahra},
booktitle={International Conference on Neural Information Processing},
pages={173--187},
year={2023},
organization={Springer}
}
Papers
| Paper | Code | Results | Date | Stars |
|---|
