ISO 24619:2022 Crane Digital Twin Technical Requirements Explained
ISO 24619:2022, "Technical Requirements for Crane Digital Twins," defines the standard for digital twin technology in cranes. It specifies data acquisition model accuracy, simulation analysis functions, and virtual-physical synchronization requirements for digital twin models, enabling design validation, operational monitoring, and predictive maintenance through a virtual mirror of the crane.
Digital Twin Model Construction
Digital twin models are built through multi-source data fusion. The 3D geometric model is created from design drawings and on-site scan data, delivering precise models of the main girder, end carriages, trolley, and mechanisms with an accuracy of ±2 mm. The finite element model is derived from the 3D model, with structural mesh generation for real-time computation of stress distribution and deformation. The kinematic model defines motion pair constraints and travel ranges for each mechanism, enabling virtual motion simulation of hoisting, luffing, slewing, and travel operations. The data mapping model links real-time sensor data—load, working radius, height, and speed—to corresponding nodes in the twin model via API interfaces, achieving virtual-physical synchronization. Model response time is ≤1 s, with state parameter deviation kept within ±2%. Kelude Heavy Industry's digital twin system is built in accordance with ISO 24619.
Application Scenarios and Use Cases
During the design phase, virtual load tests are conducted under various operating conditions before manufacturing begins, verifying structural strength and fatigue life, optimizing design parameters, shortening prototype testing cycles, and reducing development costs. In the operations and maintenance phase, the system monitors equipment status in real time, tracking main girder stress, fatigue damage accumulation, and mechanism health indices. When any parameter exceeds a preset warning threshold, the system automatically pushes maintenance recommendations, enabling predictive maintenance and preventing unexpected failures and downtime losses. For training, operators can practice crane operations on a digital twin simulator, covering normal operating conditions, emergency scenarios, and accident situations—all without requiring real equipment, reducing training risks and costs while improving training efficiency. For remote diagnostics, maintenance personnel can access real-time equipment status and historical data through the digital twin platform, working alongside on-site staff with AR-assisted maintenance support to quickly locate and resolve faults.
| Stage | Application | Value |
|---|---|---|
| design | Virtual Load Test | Shortening Prototype / Demo Unit Cycle Optimization Parameter |
| Operation & Maintenance | Condition Monitoring+Predictionmaintenance | Unexpected Downtime Reductionfault shutdown |
| Training | simulator Operator Training | Risk Reduction & Efficiency Improvement |
| Remote Diagnostics | ARAssistance Maintenance | Rapid Positioning Fault |
Data Communication & Accuracy
Sensor data is transmitted in real time from physical equipment to the digital model using OPC UA or MQTT communication protocols, ensuring seamless synchronization. With a response time of ≤1s, the Digital Twin maintains real-time consistency with its physical counterpart. State parameter deviations are held within ±2% to guarantee reliable analysis results. The 3D geometric model is built to an accuracy of ±2mm, providing a precise geometric foundation for finite element analysis and kinematic simulation. Kelude's Digital Twin system is developed in accordance with ISO 24619:2022, supporting diverse applications such as design validation, operational monitoring, and training simulation.
| Parameter | Requirement |
|---|---|
| communication protocol | OPC UA/MQTT |
| Model Response | ≤1s |
| Parameter Deviation | ≤±2% |
| 3DAccuracy | ±2mm |
| data storage | ≥3Annual Cloud |
FAQ
Q: How is a Digital Twin model built?
A: The process involves four key steps: creating a 3D geometric model (accuracy ±2mm), developing a finite element model for stress distribution analysis, building a kinematic model to simulate mechanism motion, and establishing a data mapping model that synchronizes sensor data with the virtual twin. Model response time is ≤1s with a deviation of ≤±2%.
Q: What are the main applications of Digital Twin technology?
A: Digital Twin supports the full lifecycle: virtual load testing during the design phase to validate and optimize parameters; Condition Monitoring and Predictive Maintenance during operation to prevent unexpected failures; simulator-based operator training to reduce risk; and Remote Diagnostics with AR-assisted maintenance for rapid fault localization.
Q: What communication protocols and accuracy levels are supported?
A: Real-time data transmission uses OPC UA or MQTT protocols. Model response time is ≤1s with a parameter deviation of ≤±2%. 3D model accuracy is ±2mm, and data storage retains cloud backups for a minimum of 3 years.
Q: What makes Kelude's Digital Twin system unique?
A: Kelude's system delivers 3D model accuracy of ±2mm, real-time finite element computation for stress distribution, and virtual-physical synchronization with a parameter deviation of ≤±2%. It supports design validation, operational monitoring, and training simulation across multiple scenarios, enhancing full life cycle management of equipment.
