Graph neural networks are increasingly applied to multimodal medical diagnosis for their inherent relational modeling capabilities. However, their efficacy is often compromised by the prevailing reliance on a single, static graph built from indiscriminate features, hindering the ability to model patient-specific pathological relationships. To this end, the proposed Multi-Activation Plane Interaction Graph Neural Network (MAPI-GNN) reconstructs this single-graph paradigm by learning a multifaceted graph profile from semantically disentangled feature subspaces. The framework first uncovers latent graph-aware patterns via a multi-dimensional discriminator; these patterns then guide the dynamic construction of a stack of activation graphs; and this multifaceted profile is finally aggregated and contextualized by a relational fusion engine for a robust diagnosis. Extensive experiments on two diverse tasks, comprising over 1300 patient samples, demonstrate that MAPI-GNN significantly outperforms state-of-the-art methods.

As an important component of clean energy, wind power generation is of great significance in promoting energy structure transformation and achieving sustainable development. It is particularly crucial to develop precise and effective fault diagnosis technology to ensure the safe and stable operation of wind turbines. This paper proposes a fault diagnosis method for wind power transmission systems based on model and data fusion, called PINN_Transformer. The method integrates the nonlinear dynamics model of the planetary gearbox as physical prior knowledge into a Transformer-based diagnostic network. To achieve this fusion, a novel physics-informed loss function is constructed, which maps the system’s vibration differential equations to the time-frequency domain via wavelet transform, and simultaneously incorporates constraints based on energy conservation and time-frequency feature matching. This approach embeds the governing physical laws directly into the learning process of the deep network, addressing the interpretability gap in purely data-driven methods while enhancing feature discrimination. Experimental results demonstrate significant advantages of PINN_Transformer compared to other advanced diagnostic methods, achieving a fault diagnosis accuracy of 99.84%. Furthermore, the model maintains robust performance with an accuracy above 97.07% under additive noise conditions, confirming its good engineering application value.

To address the challenge of secure federated modeling in fault diagnosis under imbalanced data scenarios for multi-railway high-speed train bogies, this study proposes a multi-rank federated distillation (MFD) framework aimed at enhancing the generalization capacity of clients with limited sample sizes. First, the MFD framework is designed to perform multiple distillation tasks, with each task’s loss function decoupled into two components to balance losses between target and non-target classes. Second, an adaptive weight adjustment strategy is introduced to efficiently train models by coordinating the loss outputs across these tasks. Third, to mitigate the learning costs associated with the MFD, clients share a foundational shallow network via model transfer while incorporating personalized modules to improve adaptability. By validating the proposed framework on datasets from high-speed train bogies across multiple railways, this study demonstrates its effectiveness in addressing challenges associated with secure federated modeling while maintaining satisfactory diagnostic performance. The findings present a viable solution for implementing federated learning among clients with imbalanced data in industrial applications.

In the context of high-speed trains (HST) bogie fault diagnosis, most existing state-of-the-art approaches struggle to effectively identify engineering fault types that lack historical records, leading to issues such as insufficient feature learning and high misdiagnosis rates. To tackle the challenges, this article introduces a generalized zero-shot learning (GZSL) strategy and presents a fault diagnosis framework for HST bogies, referred to as “ResDDPM-GZSL.” The study initially designs and constructs a foundational attribute description matrix for HST bogies. Residual networks are utilized to extract data features, which facilitates the bidirectional mapping among data, attributes, and features. Furthermore, the structure of the diffusion model is enhanced and customized for better adaptation to low-dimensional data, thereby improving the capability of the model to efficiently learn and generate latent features of unknown data, while maintaining stability. Finally, the model is established based on known data feature extraction and unknown data feature generation. Experimental results demonstrate that the average diagnostic accuracy for known fault classes exceeds 95%, while the average diagnostic accuracy for unknown fault classes surpasses 70%, with a harmonic mean diagnostic accuracy exceeding 80%. The results obtained significantly surpass those of other generative method-based diagnostic approaches, indicating that the study offers an effective solution for zero-shot learning fault diagnosis.

Due to device operating environment limitations and data privacy protection, it is frequently difficult to obtain sufficient high-quality labeled data from devices, resulting in an insufficient generalization ability of fault diagnosis model. Therefore, a high-performance federated learning framework is proposed in this work, which makes improvements in the procedure of model aggregation and local model training. In the model aggregation of central server, an optimization aggregation strategy in which forgetting Kalman filter (FKF) is combined with cubic exponential smoothing (CES) is proposed to improve the efficiency of federated learning. In the local model training of multiclient, a deep learning network combined with multiscale convolution, attention mechanism, and multistage residual connection is proposed, which is able to fully extract multiclient data features simultaneously. Meanwhile, experiments on two machinery fault datasets show that the proposed framework is capable of achieving high accuracy and strong generalization of fault diagnosis on the premise of protecting data privacy in actual industrial situations.

In this article, an adaptive iterative learning control scheme is presented for a class of nonlinear parametric strict-feedback systems with unknown state delays, aiming to achieve the point-wise tracking of desired trajectory in a finite interval. The appropriate Lyapunov-Krasovskii functions are established to compensate the influence of time-delay uncertainties on the control systems. As the main features, the proposed approach integrates the command filter into the backstepping procedure to avoid the differential explosion problem that may occur with the increase of system order, and introduces the hyperbolic tangent functions into the learning controller to handle the singularity problem thus maintaining the continuity of input signal. The results of theoretical analysis and numerical simulation demonstrate that the tracking errors at the entire period will converge to a compact set along the iteration axis. Compared with the existing works, the proposed control scheme is promising to manifest the better performance and practicability owing to the learning mechanism, the dynamic model, as well as the implementation of controller.

This article considers the speed tracking control problem for high-speed train systems (HSTs) under the condition of passing through neutral sections in the presence of parametric uncertainties. Noticing the prominent feature of HSTs operation, i.e., the spatial repetitiveness, a novel spatial iterative learning control (ILC) scheme is proposed. First, the motion dynamic model of HSTs is constructed with the aid of temporal-spatial conversion. Meanwhile, input saturation constraint is introduced to address the limitation of system power supply capability and the loss of traction/braking force in neutral section. Then, the ILC law and the associated parametric updating law are devised to address the system uncertainties and realize the adaptive tracking control simultaneously. The stability of the closed-loop system and the convergence of the tracking errors are confirmed based on a space-weighted Lyapunov–Krasovskii-like composite energy function (CEF). Finally, numerical simulations are performed to illustrate the effectiveness of the proposed control scheme.

Fault diagnosis of high-speed train (HST) bogie plays an indispensable role in guaranteeing the safety and stability of the daily operation of HST. Up to now, the well-established fault diagnosis models of HST bogie, which are usually trained by supervised learning mechanism, require that the training and testing data satisfy the same probability distribution. Consequently, those methods could lose their efficacy when the probability distribution of the testing data is changed owing to the variation of HST running speed and the labeled data corresponding to the changed speed is absent. To address this, a stepwise adaptive convolutional network (SACN) is proposed to learn the domain-invariant features of vibration data in different speed domains. Moreover, a novel in-and-out stepwise transfer method is designed to deal with the scenario of continuous change in vehicle speed. The experimental tests of the proposed method are conducted using the dataset acquired by SIMPACK via the HST model CRH380 A, mainly addressing the single and compound failure classification problem of the three key components of HST bogie, namely, the air spring, antiyaw damper, and lateral damper. Overall, the proposed SACN achieves an average accuracy of 96.1%, demonstrating the remarkable performance of domain adaptation in performing fault diagnosis of HST bogie under variant running speeds.

The lateral vibration of high-speed train reduces the ride stability and passenger comfort. Semi-active suspension system is often adopted to suppress the lateral vibration of trains due to its low cost and low energy consumption in control, where magnetorheological (MR) damper is the main vibration absorber. In this paper, a full model of the controlled system that consists of the 3-degree of freedom train model and the hysteretic MR damper model is first built. Then, a second-order sliding mode (SOSM) controller is designed for the suppression of lateral vibration of train, where the hysteresis and non-hysteresis nonlinearities are addressed simultaneously and rigorously. Finally, comparative simulations are conducted under the excitation of the German low-interference track spectrum. The results show that the proposed controller can suppress the lateral vibration of high-speed train efficiently in the sense that, compared with the passive control, the internal model-based resonant control and repetitive learning control, the suspension ratio of root-mean-square of lateral acceleration is reduced by 96.98%, 35.70% and 10.54%, respectively.

As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying the performance degradation of bogie is critical in determining a particular maintenance strategy. In this article, by considering the intrinsic link between fault type and performance degradation of bogie, a novel multiple convolutional recurrent neural network (M-CRNN) that consists of two CRNN frameworks is proposed for simultaneous diagnosis of fault type and performance degradation state. Specifically, the CRNN framework 1 is designed to detect the fault types of the bogie. Meanwhile, CRNN framework 2, which is formed by CRNN Framework 1 and an RNN module, is adopted to further extract the features of fault performance degradation. It is worth highlighting that M-CRNN extends the structure of traditional neural networks and makes full use of the temporal correlation of performance degradation and model fault types. The effectiveness of the proposed M-CRNN algorithm is tested via the HST model CRH380A at different running speeds, including 160, 200, and 220 km/h. The overall accuracy of M-CRNN, i.e., the product of the accuracies for identifying the fault types and evaluating the fault performance degradation, is beyond 94.6% in all cases. This clearly demonstrates the potential applicability of the proposed method for multiple fault diagnosis tasks of HST bogie system.

Fault diagnosis of high-speed train (HST) bogie is essential in guaranteeing the normal daily operation of an HST. In prior works, feature extraction from multisensor vibration signals mainly relies on signal processing methods, which is independent of the classification process. Based on convolutional neural networks (CNNs), this paper presents a novel fault diagnosis system using the residual-squeeze net (RSNet), which is directly applicable to raw data (time sequences) and does not require any signal transformation or postprocessing. In this network, information fusion is achieved by using the convolutional layer. More specifically, via the squeeze operation, an optimal combination of channels is learnt by training the network. Experimental results obtained by using SIMPACK simulation data demonstrate the effectiveness of the proposed approach in both complete failure case and single failure case, with diagnosis accuracy near 100%. The proposed approach also shows good performance in identifying the locations of faulty components. Comparisons between RSNet and competitive methods shows the advantages of RSNet for fault classification.