%0 Generic %1 truchan_2024_8238653 %A Truchan, Hubert %A Admadi, Zahra %D 2024 %I Zenodo %K myown %R 10.5281/zenodo.8238653 %T Mudestreda Multimodal Device State Recognition Dataset %U https://doi.org/10.5281/zenodo.8238653 %X The rapid advancement in multimodal datasets primarily featuring images, audio, and text, coupled with transformer-based architectures, has been undeniable. However, many real-world industrial scenarios necessitate a distinct approach, focusing on industrial-grade imagery and multivariate time series data from sensors, suitable for deployment on efficient edge devices. This shift is driven by two major limitations of existing multimodal open-source datasets: (i) they are often constrained to specific modalities, typically centered around video data, limiting their adaptability, and (ii) they predominantly feature general-purpose objects, which do not address domain-specific and industrial challenges effectively. Addressing these gaps, we introduce the Mudestreda dataset, a novel multimodal industrial device state recognition resource. Mudestreda uniquely showcases the multimodal nature of industrial tool wear, comprising 512 samples with four-dimensional observations: three force signal sequences and one RGB image of a shaft milling tool, collected over five weeks from a Production Centrum.

@dataset{truchan_2024_8238653, abstract = {The rapid advancement in multimodal datasets primarily featuring images, audio, and text, coupled with transformer-based architectures, has been undeniable. However, many real-world industrial scenarios necessitate a distinct approach, focusing on industrial-grade imagery and multivariate time series data from sensors, suitable for deployment on efficient edge devices. This shift is driven by two major limitations of existing multimodal open-source datasets: (i) they are often constrained to specific modalities, typically centered around video data, limiting their adaptability, and (ii) they predominantly feature general-purpose objects, which do not address domain-specific and industrial challenges effectively. Addressing these gaps, we introduce the Mudestreda dataset, a novel multimodal industrial device state recognition resource. Mudestreda uniquely showcases the multimodal nature of industrial tool wear, comprising 512 samples with four-dimensional observations: three force signal sequences and one RGB image of a shaft milling tool, collected over five weeks from a Production Centrum.}, added-at = {2024-02-18T21:01:11.000+0100}, author = {Truchan, Hubert and Admadi, Zahra}, biburl = {https://www.bibsonomy.org/bibtex/25f9628cd1456e6c2a20706fa8132b9e6/truchan}, doi = {10.5281/zenodo.8238653}, interhash = {144bdc9af772a5331ea910e82320b83e}, intrahash = {5f9628cd1456e6c2a20706fa8132b9e6}, keywords = {myown}, month = jan, publisher = {Zenodo}, timestamp = {2024-02-18T21:01:11.000+0100}, title = {Mudestreda Multimodal Device State Recognition Dataset}, url = {https://doi.org/10.5281/zenodo.8238653}, year = 2024 }