Novel Hybrid Physics-Informed Deep Neural Network for Dynamic Load Prediction of Electric Cable Shovel

Abstract Electric cable shovel (ECS) is a complex production equipment, which is widely utilized in open-pit mines. Rational valuations of load is the foundation for the development of intelligent or unmanned ECS, since it directly influences the planning of digging trajectories and energy consumption. Load prediction of ECS mainly consists of two types of methods: physics-based modeling and data-driven methods. The former approach is based on known physical laws, usually, it is necessarily approximations of reality due to incomplete knowledge of certain processes, which introduces bias. The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization, which introduces variance. In addition, some parts of load are non-observable and latent, which cannot be measured from actual system sensing, so they can’t be predicted by data-driven methods. Herein, an innovative hybrid physics-informed deep neural network (HPINN) architecture, which combines physics-based models and data-driven methods to predict dynamic load of ECS, is presented. In the proposed framework, some parts of the theoretical model are incorporated, while capturing the difficult-to-model part by training a highly expressive approximator with data. Prior physics knowledge, such as Lagrangian mechanics and the conservation of energy, is considered extra constraints, and embedded in the overall loss function to enforce model training in a feasible solution space. The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.