Vibration analysis of an experimental double bridge crane system with artificial neural networks

Abstract

In crane systems, lifting, carrying and lowering the load from one place have different dynamic effects on the system. One of these dynamic effects is the moving load problem caused by the movement of the load on the crane system. With the increasing technology in recent years, production speeds have increased. For this reason, it has made the requirements for fast-running cranes mandatory for the transportation and loading of products. Therefore, it is important to know the dynamic effects of the moving load in fast working conditions. In this experimental study, the dynamic effects occurring on the crane beams with different loads and different working speeds during the transportation of the load on the crane are analysed. Here, there are multiple cars on the crane, and these cars are designed in different numbers on the crane and can be operated at different speeds. Under these conditions, the dynamic effects that have arisen have been tested. Also, vibration measurements were carried out at different points on the bridges. And then, these parameters obtained were used in two different proposed neural network types to predict the vibrations that occur on the crane system. Simulation results show that two approaches suggested that a radial basis neural network type can be used as an adaptive predictor for such systems in the experimental applications.