Research on a dynamic operational safety state perception biomechanics-inspired method for gantry cranes driven by numerical-physical model fusion
Abstract
Hydraulic gantry cranes (hereinafter referred to as “gantry cranes”) are highly susceptible to instability during dynamic operations due to high-speed unsteady airflow in mountainous and canyon areas, leading to safety risks such as derailment and overturning. Traditional single data-driven or model-driven methods fall short in ensuring real-time performance, accuracy, and comprehensiveness for the safety state perception of gantry cranes during dynamic operations. To overcome this, we draw inspiration from the way biomechanics integrates multiple data sources and models. A digital prototype of the gantry crane was established, mimicking the creation of a virtual model of a biological structure for in-depth analysis. A surrogate model for the dynamic response of the gantry crane under the coupled effects of wind load, lifting load, and self-driving force was constructed. In biomechanics, models are developed to simulate the combined actions of different forces on biological tissues and organs. Here, we approach the gantry crane’s force analysis in a similar fashion, considering the complex interactions of various loads. Based on this, a data model fusion driven method for safety state perception during dynamic operations of gantry cranes was proposed. This method is in line with the practice in biomechanics of integrating experimental data and theoretical models to gain a more complete understanding of biological processes. By fusing data and models, we aim to enhance the safety state perception of gantry cranes, just as biomechanics uses integrated approaches to improve our understanding of biological systems. Simulation results of a 150 t gantry crane at a hydropower station demonstrate the feasibility and practicality of the proposed method. This validation process is comparable to how biomechanical models are tested and verified through experiments on biological specimens or simulations of biological movements, providing evidence for the effectiveness of our approach inspired by biomechanics.
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