Mudestreda
Full name: 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}  
}  
```