In recent years, causal learning has provided application prospects for revealing the internal causal relationships of equipment and the explainability of intelligent diagnostic models. However, existing methods still have limitations of the difficulty in eliminating spurious causal correlations in high-dimensional data and insufficient explainability, leading to unstable and unreliable diagnostic performance in unseen domains. Aiming at the above problems, an interpretable fault diagnosis method based on causal invariant graph neural network (CIGNN) is proposed to enhance model’s accuracy and interpretability for gears in the unseen domain. Firstly, a structural causal model is constructed from the cross-domain perspective and combined with GNN to clarify the internal causal mechanism of faults. Then, a causal disentanglement refining module is proposed to separate the effective causal parts from the high-dimensional and complex GNN. Furthermore, a domain causal feature consistency method is proposed to guide CIGNN in learning consistent causal feature embeddings across multi-source domains. Finally, a causal intervention risk minimization strategy is introduced to enable CIGNN deeply mine potential features and block the interference of backdoor paths, enhancing diagnostic stability. Experimental results reveal that the proposed CIGNN model performs robustly in the unseen domain diagnosis task and provides interpretable explanation for decision-making in engineering applications.

Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit system. Since the metro or other rail transit have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing train bogie-bearings under variable working conditions is challenging due to wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for bogie-bearing fault diagnosis is proposed to deal with the above problems. A third-order tensor model is established to be suitable for variable working conditions. Furthermore, a density-based affinity propagation tensor (DAP-Tensor) clustering algorithm is presented to identify different failures with unlabeled. Train bogie and public data sets were employed to simulate three probable conditions of train operation: high-frequency impact, speed variation, and load change. Compared with existing clustering methods in three cases, the proposed DAP-Tensor performs better in identifying bearing faults under variable working conditions. Moreover, The DAP-tensor has a comparable recognition rate to some deep learning methods, which unsupervised characteristics show it has potential for applications on rail transit trains.

Recently, the cybertwin-driven intelligent internet of vehicles has received widespread consideration in modern smart cities which makes it possible to run high dimensional, low-latency tolerating, and computational-intensive tasks on the vehicles. Thanks to the development in mobile edge computing, the so-called vehicular edge computing allows mobile vehicles to offload their tasks to the road-side unit or hybrid access point due to the limited computation capability. In this paper, we consider a cybertwin-driven internet of vehicle system that provides computing services for mobile vehicles in local area network or wide area network aided with multi-intelligent reflecting surfaces. Based on this system model, we investigate an optimization problem to jointly maximize the sum of data rate in wide area network, and the sum of energy utilities of vehicles. However, in the proposed system model, it is complicated to design the optimal phase, scheduling and offloading decision policy. To solve this issue, we propose a block coordinate descent and deep reinforcement learning based intelligent IoV computing policy. Numerical results have verified that the proposed algorithm can achieve better IoV computing performance compared with four relative benchmark algorithms.