Digital twin based condition monitoring of a knuckle boom crane: An experimental study

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.