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))