{"url":"/dataset/mudestreda","name":"Mudestreda","full_name":"Mudestreda Multimodal Device State Recognition Dataset","description_markdown":"# Mudestreda Multimodal Device State Recognition Dataset\r\nobtained 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.\r\n\r\nThe official dataset used in the **paper** \"Multimodal Isotropic Neural Architecture with Patch Embedding\" ICONIP23.  \r\nOfficial Minape **repository**:  https://github.com/hubtru/Minape  \r\nOfficial Mudestreda **dataset**:  https://zenodo.org/records/8238653  \r\nConference **paper**:  https://link.springer.com/chapter/10.1007/978-981-99-8079-6_14  \r\n**Mudestreda (MD)** | Size 512 Samples (Instances, Observations)| Modalities 4 | Classes 3 |  \r\n**Future research:** Regression, Remaining Useful Life (RUL) estimation, Signal Drift detection, Anomaly Detection, Multivariate Time Series Prediction, and Feature Engineering.\r\n\r\n## Overview\r\n* Task: Uni/Multi-Modal Classification \r\n* Domain: Industrial Flank Tool Wear of the Milling Machine\r\n* Input (sample): 4 Images: 1 Tool Image, 3 Spectrograms (X, Y, Z axis)\r\n* Output: Machine state classes: `Sharp`, `Used`, `Dulled`\r\n* Evaluation: Accuracies, Precision, Recal, F1-score, ROC curve\r\n* Each tool's wear is categorized sequentially: Sharp → Used → Dulled.\r\n* The dataset includes measurements from ten tools: T1 to T10.\r\n* Data splitting options include random or chronological distribution, without shuffling.\r\n* Options: \r\n  * Original data or Augmented data\r\n  * Random distribution or Tool Distribution \r\n\r\n## Use Cases:  \r\n\r\n| **Input**      | **Model**      | **Output**           |\r\n|-----------------|---------------|-----------------------|\r\n| **Use Cases:**            |                  |                                  |\r\n| 4 Images (1 Tool Image, 3 Spectrograms (X, Y, Z)) | Classification Model       |  Class (Flank Tool Wear: `Sharp`, `Used`, `Dulled`)    |\r\n| 3 Spectrograms (X,Y,Z axis)                       | Classification Model       |  Class (Flank Tool Wear)                               |\r\n| 1 Tool Image                                      | Classification Model       | Image Class (Flank Tool Wear)                               |\r\n| **Future Work:**                                  |                            |                                                       |\r\n| [1, ..., 4] Images                                | Model                      | Remaining Useful Life (RUL) estimation                |\r\n| [1, ..., 4] Images                                | Monitoring Model                      | Fault and Anomaly Detection                           |\r\n| [1, ..., 4] Images                                | Forecasting Model                      | Multivariate Time Series Prediction                |\r\n| [1, ..., 3] Spectrograms                          | Model                      | Signal Drift measurement                              |\r\n| [1, ..., 4] Images                                | Regression Model           | Zero-Shot Flank Tool Wear (in µm, 10e-6 meter)        |\r\n| [1, ..., 4] Images                                | Feature Engineering        | Diagnostic Feature Designer       \r\n\r\n\r\nIf you use Mudestreda dataset cite the work Minape @ ICONIP2023.\r\n\r\n## Cite the Paper: \r\nIf you reference the papr or you use Mudestreda dataset cite the work `Minape @ ICONIP2023`\r\n```\r\n@inproceedings{truchan2023multimodal,  \r\n  title={Multimodal Isotropic Neural Architecture with Patch Embedding},  \r\n  author={Truchan, Hubert and Naumov, Evgenii and Abedin, Rezaul and Palmer, Gregory and Ahmadi, Zahra},  \r\n  booktitle={International Conference on Neural Information Processing},  \r\n  pages={173--187},  \r\n  year={2023},  \r\n  organization={Springer}  \r\n}  \r\n```","description_withheld":null,"homepage":"https://zenodo.org/records/8238653","introduced_date":"2024-01-24","introduced_date_note":null,"introduced_by":null,"license":{"name":"GNU General Public License v3.0 or later","url":"https://www.gnu.org/licenses/gpl-3.0-standalone.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Audio Classification","url":"/task/audio-classification","datasets_with_task":"/datasets/task/audio-classification"},{"name":"Multimodal Deep Learning","url":"/task/multimodal-deep-learning","datasets_with_task":"/datasets/task/multimodal-deep-learning"},{"name":"Sequential Image Classification","url":"/task/sequential-image-classification","datasets_with_task":"/datasets/task/sequential-image-classification"},{"name":"Multi-modal Classification","url":"/task/multi-modal-classification","datasets_with_task":"/datasets/task/multi-modal-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Mudestreda"],"data_loaders":[{"repo":"https://github.com/hubtru/Minape","url":"https://github.com/hubtru/Minape","frameworks":["tf"]}],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}