Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems, great challenges which may prevent successful FDD implementations on practical high-speed trains are then summarized in detail. Benefiting from theoretical developments of data-driven FDD strategies, instructive perspectives on this topic are further elaborately conceived by the integration of model-based FDD issues, system identification techniques, and new machine learning tools, which provide several promising solutions to FDD strategies for traction systems in high-speed trains.

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.

This paper investigates the fixed-time time-varying formation control problems for heterogeneous multi-agent systems (MASs) composed of multiple Unmanned Ground Vehicles (UGVs) and multiple Unmanned Aerial Vehicles (UAVs) in the presence of actuator faults, parameter uncertainties, matched and mismatched disturbances. Besides achieving the desired formation configurations, each follower can also track the position trajectory produced by the virtual leader within fixed time simultaneously. The difference dynamic characteristics between the heterogeneous agents leads to unbalanced interaction of lumped uncertainties in the communication network, which increases the difficulty of collaborative control. To estimate the mismatched disturbances and lumped uncertainties, a fixed-time observer for each follower is designed, which can guarantee the estimation errors converge to the origin in fixed settling time. Subsequently, by utilizing the backstepping technique and the fixed-time stability theory, an observer-based distributed fixed-time formation controller for each follower in the X- Y axes and the observer-based decentralized fixed-time tracking controllers for follower-UAVs in the Z axes are presented, which are shown to be fixed-time stable even under the influence of actuator faults and mismatched disturbances. Moreover, the fixed-time results can ensure the convergence time is independent of initial conditions. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithms.

In this paper, a new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem. The paper focuses on the distributed plant-wide processes. The core of the proposed method is to reduce uncertainties using correlation information from the neighboring nodes. Furthermore, the cost of data transmission between network nodes is also reduced by the D-CCA algorithm. When the proposed method and the existing methods are compared using the Tennessee Eastman benchmark process, the false alarm rate, fault detection rate, and the detection delay are comparable. This suggests that the proposed method is feasible.

Emerging intelligent fault diagnosis models based on domain adaptation can resolve domain shift problems produced by different working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address this challenge, a deep mixed domain generalization network (DMDGN) is proposed for intelligent fault diagnosis. In this novel model, data augmentation is applied to both class and domain spaces, adversarial learning is employed to introduce adversarial perturbations, and a domain-based discrepancy metric is used to balance intra- and interdomain distances. The model can effectively learn more domain-invariant and discriminative features from multiple source domains to perform different generalization tasks for different working loads and machines. The feasibility of the DMDGN model is verified on two public datasets and one private dataset collected from practical production processes. Empirical results show that the DMDGN model outperforms several state-of-the-art models.