Leader
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Hosting Facilities
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DART
Data Collection
Digital-twin & Augmented-sensing for Rope Tension & spooling analysis: extend rope life, cut waste, increase safety
Challenge: Augmented sensing solution
Project DART develops an augmented sensing solution that estimates hard-to- measure engineering quantities—such as rope tension, multi-wrap contact stresses, and spooling cycles—from camera images fused with a physics-based digital twin. Building upon Raidyn’s core computer vision layers, the system utilizes real-time video vibrometry and geometry regression alongside a lightweight, edge-deployed structural model. This enables continuous, uncertainty-aware health monitoring, turning raw operational signals into actionable Remaining Useful Life (RUL) estimates and explainable maintenance alerts to optimize safety, cut material waste, and reduce asset downtime.
Leader
Hosting Facilities
The solution will be validated within an operational high-TRL industrial pilot environment at Teufelberger-Redaelli in Austria. This site provides a representative, high-exposure ropeway application specifically suited for testing complex rope transport systems and automated drum spooling contexts under real, continuous working conditions. A second industrial application is currently being selected
Background
Steel wire ropes move high-value loads and people daily, yet industrial inspection practices remain heavily interval-based, manual, and error-prone. This operational approach leads to premature asset replacement or major downtime incidents when sub-surface or sudden degradation is missed. Crucial damage drivers like continuous axial tension, localized contact pressures, and bending cycles over drums cannot be effectively tracked with traditional intrusive hardware sensors. Raidyn addresses this operational gap by replacing static inspections with automated, non-intrusive edge-sensing architecture to measure hidden forces continuously.
Solution
DART deploys an end-to-end edge pipeline running on compact GPU hardware (NVIDIA Jetson) coupled to global-shutter industrial cameras. The system monitors two distinct regions : a free-span view to capture load-dependent geometry and track high-frequency modal shifts via video vibrometry , and a drum view tracking real-time spooling state, wrapping index, and localized slip. These multi-path visual features feed directly into an on-edge data-model fusion layer, combining real-time perception with a lightweight, physics- informed beam formulation digital twin. The digital twin maps complex structural kinematics, tracks localized wrap stresses, and propagates estimation uncertainties dynamically. Finally, an AI decision support layer ingests these continuous stress states alongside operational context to compute reliable Remaining Useful Life (RUL) windows, track cumulative damage, and issue auditable, explainable maintenance and lubrication schedules directly to site operators
Objectives
- Deliver continuous, non-intrusive virtual sensors tracking multi-wrap spooling and rope tension within a 10% absolute accuracy band
- Maintain an on-edge operational tracking coverage of 95% under variable industrial lighting and field conditions
- Translate real-time stress signals into explicit, uncertainty-gated operator recommendations
- Formally validate a target 30% reduction in rope-related machinery stops.
KPIs achieved
Development
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Feedback
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Mauris et nulla tellus. Nulla consectetur placerat nunc placerat bibendum. Sed sit amet consequat ex. Pellentesque id odio felis. Etiam tellus dui, maximus non vehicula ut, suscipit vel ex. Maecenas quis lorem ut sem aliquet fermentum.
Mauris et nulla tellus. Nulla consectetur placerat nunc placerat bibendum. Sed sit amet consequat ex. Pellentesque id odio felis. Etiam tellus dui, maximus non vehicula ut, suscipit vel ex. Maecenas quis lorem ut sem aliquet fermentum.