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.
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