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 repository: https://github.com/hubtru/Minape  

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.

Notice: Tables and images do not render properly. 

Recommended: `README.md` includes the Mudestreda description and images `Mudestreda.png`  and `Mudestreda_Stage.png`.

Data 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 ([see Dataset Splitting](#dataset-spliting))