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• A novel PINN architecture replicates electromagnetic responses of electric motors. • PDEs with rotational coordinate transformation well supervise a neural network. • Separated networks for a rotor and stator with interface loss improve the accuracy. • Learning rate annealing improves accuracy and robustness of the PINN. • The PINN is the faster yet similar accuracy than the finite element analysis. This study presents a novel physics-informed neural network (PINN) architecture designed to address the challenges of replicating an electric motor. The proposed architecture has three key features. First, it uses three partial differential equations with rotational coordinate transformation to supervise the neural network during training with limited data, which improves the accuracy of the solution. One of the differential equations is expressed in variational form to effectively compute the numerical integration. Second, separate networks are proposed for the rotor and stator domains due to their distinct characteristics during operation, namely, that the rotor rotates while the stator remains fixed. An interface loss is included in the entire loss function to compensate for the significant discontinuity and incompatibility between the separate networks when estimating the results of both domains. Third, a learning rate annealing method is introduced to update the adaptive weights of each loss term, thus improving the accuracy and robustness during the training of the neural network. The performance of the proposed PINN was validated using electromagnetic response datasets obtained from both measurements and finite element analyses. Systematic analysis demonstrated that the three features significantly improved the accuracy and robustness of the neural network when estimating the electromagnetic responses of an electric motor. Furthermore, the inference time of the PINN is ten times faster than that of a finite element analysis with a similar level of accuracy, making it suitable for control and design purposes in various real-world applications. Consequently, the versatility of the proposed PINN can accelerate the development of digital twins for intelligent systems by deploying an electric motor, and it could also be used for prognostics and health management because it can estimate electromagnetic responses under both normal and failure conditions. [ABSTRACT FROM AUTHOR]

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities. [ABSTRACT FROM AUTHOR]