Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

Summary In this article, we propose a deep Koopman model predictive control (MPC) strategy to improve the transient stability of power grids in a fully data‐driven manner. Due to the high‐dimensionality and the nonlinearity of the transient process, we use the Koopman operator to map the original nonlinear dynamics into an infinite dimensional linear system. To facilitate the control design, we first utilize the deep neural network method to efficiently train observable functions to approximate the Koopman operator so that the obtained dynamics in the high dimensional space is a linear system. We then propose an MPC strategy for the obtained high dimensional linear system. The proposed control strategy utilizes energy storage units, which inject or absorb real power at the synchronous generator buses to enhance the transient stability. Simulation studies implemented on the IEEE 9‐bus 3‐machine test system and the IEEE 39‐bus 10‐machine test system illustrate the performance of the proposed deep Koopman MPC strategy. The results demonstrate that the proposed control strategy effectively enhances the transient stability of the system even in the presence of severe faults.

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Additive manufacturing is becoming increasingly popular because of its unique advantages, especially fused deposition modelling (FDM) which has been widely used due to its simplicity and comparatively low price. However, in current FDM processes, it is difficult to fabricate parts with highly accurate dimensions. One of the reasons is due to the slicing process of 3D models. Current slicing software divides the parts into layers and then lines (paths) based on a fixed value. However, in a real printing process, the printed line width will change when the process parameters are set in different values. The various printed widths may result in inaccuracy of printed dimensions of parts if using a fixed value for slicing. In this paper, a mathematical model is proposed to predict the printed line width in different layer heights. Based on this model, a method is proposed for calculating the optimal width value for slicing 3D parts. In the future, the proposed mathematical model can be integrated into slicing software to slice 3D models for precision additive manufacturing.

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