Application of Big Data Technology in Ship-to-Shore Quay Cranes at Smart Port

Abstract

As more and more container terminals are becoming intelligent, different kinds of sensors are widely installed at different locations of the cranes and collect a large amount of data. In order to effectively utilize and manage these huge amounts of actual working data of different sensors and grasp the status of the terminal, this article proposes a data processing framework that integrates the crane load, energy consumption, crane trolley speed and crane gearbox vibration signals of the container terminal. Firstly, the load spectrum of the crane load is calculated by the non-parametric density estimation method in probabilistic statistics and the energy consumption curves are obtained. Secondly, the driving cycle of the crane trolley speed are constructed by drawing on the method in the transportation field. Finally, the vibration signals of the crane gearbox are used for anomaly detection by unsupervised methods; at the same time, clustering results can also be used as the basis for extracting typical vibration signals and removing redundant data.