Graph neural networks (GNNs) have emerged as a powerful tool for fault diagnosis in industrial processes, where components and their relationships can be modeled as nodes and edges in a graph. However, most GNN models are based on the I.I.D. hypothesis and learn both causal and spurious correlations. In real-world applications, test data distributions often differ from training data, leading to changes in spurious correlations and inaccurate diagnosis results. To address this issue, this paper proposes a causal regularization graph attention network (CR-GAT) that decouples causal and confounding variables and eliminates spurious correlations. The method involves extracting high-level graph variables using differentiable pooling, measuring nonlinear dependence between variables with the Hilbert-Schmidt Independence Criterion (HSIC), and iteratively optimizing GAT loss and variable weights to learn true causal relationships. Comparative experiments on three-phase flow and power system datasets demonstrate the effectiveness of the proposed method.

Traditional intelligent fault diagnosis models are usually capable of diagnosing known types of faults. However, in the field of industrial fault diagnosis in open environments, it is almost impossible to collect training samples that cover all fault categories. Therefore, when encountering unknown types of fault, traditional methods tend to misclassify them as known categories. To address this issue, a causal counterfactual faithfulness generation method is proposed for open-set fault diagnosis of complex industrial processes. Initially, the signal data from fault sensors are processed into graph data composed of nodes and edges. Then, the features of nodes and their adjacent nodes are learned and integrated into graph architecture to generate new fault sample attributes. Subsequently, the causal generative model infers the category features and combines known fault categories to generate counterfactual samples. Finally, the sample’s classification as an unknown category is ultimately determined by testing the principle of consistency. The proposed method can significantly improve the accuracy of open-set diagnosis without affecting the accuracy of closed-set classification. Comparison experiments with multiple baseline models in two fault datasets illustrated that the proposed method shows an improvement in almost all indicators, which ultimately verified the effectiveness of the proposed method in the task of fault diagnosis in open environments.

Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://github.com/IRMVLab/LHMap-loc.