Abstract
This paper presents a novel approach for implementation of a digital twin for condition monitoring of a small-scale knuckle boom crane. The digital twin of the crane is simulated real-time in a nonlinear finite element (FE) program, where the estimated payload weight is used as an input. We implement an inverse method for estimation of the weight as well as its force vector direction based on physical strain gauge measurements. Additional strain gauges were utilized for validation of accuracy of the digital twin and inverse method. Based on a few physical sensor outputs, the digital twin allows for real-time determination of stresses, strains and loads at an unlimited number of hot spots. Therefore a digital twin can be an effective tool for predictive maintenance and product life-cycle management. In addition, condition monitoring of cranes during heavy-lift operations increases safety and reliability.The presented approach is described in a general manner and is applicable for various robotic manipulators used in the industry.
Highlights
Cranes are indispensable in many industrial operations
Industrial cranes as well as robotic manipulators are often an important part of the manufacturing process, and the proposed solution can be integrated into digital twin of an entire production line, which would contribute to efficiency and optimisation
We propose a novel inverse method to meet the demands of digital twins
Summary
Cranes are indispensable in many industrial operations. Offshore cranes are both used for installation operations and are an important part of supply chains of offshore installations. Application of digital twins for condition monitoring, predictive maintenance and life-cycle management can be a reasonable solution, as, based on data from only few sensors, it provides real-time stresses in the entire structure and loads in the components such as bearings, shafts, cables, hydraulic cylinders, etc. Such data can be recorded and stored throughout the life-time of the machine. The main contribution of this work is a novel approach for implementation of a digital twin monitoring solution for a knuckle boom crane, which is applicable for other robotic manipulators used in the industrial processes. Industrial cranes as well as robotic manipulators are often an important part of the manufacturing process, and the proposed solution can be integrated into digital twin of an entire production line, which would contribute to efficiency and optimisation
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