Deployment of edge-lightweight AI vertical models in multimodal anomaly detection and energy optimization of tech-long production line - Nature
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We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.
AbstractAiming at the problems that the abnormal state of high-speed liquid packaging production lines is difficult to identify in time, the computing power of edge controller is limited, and the energy consumption regulation and condition monitoring are separated from each other, a multimodal lightweight artificial intelligence (AI) system embedded in Tech-Long production controller is constructed; this realizes the collaborative optimization of abnormal operation window identification and energy consumption of the whole line. First, this study collects vibration, acoustic, and thermal signals from the bottle blowing, filling, and packaging sections; it also constructs cross-sectional anomaly characterization through time synchronization, feature-level alignment, and hierarchical fusion.
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