Fanghua Chen

Accurate risk prediction remains a critical challenge in reliability engineering and system safety, particularly in complex systems characterized by interdependent temporal progression and spatial co-occurrence patterns. While existing approaches predominantly focus on either temporal dynamics or co-occurrence relationships, this study introduces a novel spatio-temporal graph learning architecture. First, a dual-matrix graph construction mechanism simultaneously captures spatial risk correlations through co-occurrence frequency analysis and temporal progression patterns using transition probability modeling. Second, an adaptive subgraph extraction module generates system-specific topological representations that preserve both localized risk clusters and directed temporal pathways. Third, a dual-channel graph convolutional network with bilinear interaction fusion facilitates synergistic processing of spatial coexistence features and temporal progression patterns while preserving modality-specific characteristics. Empirical validation across medical diagnosis and vehicular risk domains demonstrates the model’s effectiveness in handling multi-risk coexistence scenarios and long-term progression patterns, significantly outperforming conventional single-modality approaches. The proposed methodology offers a generalizable solution for cross-domain risk prediction tasks. The source code is available at https://github.com/FanghuaX/HS-TGN.

The implementation of predictive maintenance (PdM) represents a pivotal strategy for enhancing operational efficiency and reducing costs within the automotive industry. While extensive research has yielded sophisticated machine learning and deep learning models capable of accurately predicting failures in specific vehicle components—including suspensions and batteries—these approaches predominantly operate in isolation. A principal challenge remains the transition from predicting failures in single components to managing the health of the vehicle as an integrated system. This review systematically examines recent advances in forecasting methodologies for vehicle service parts and predictive maintenance technologies, while critically evaluating the strengths and limitations of diverse approaches. The analysis highlights crucial unresolved technical and strategic challenges that must be addressed to realize fully integrated predictive maintenance systems for next-generation vehicles, with particular emphasis on the transition from isolated component monitoring to holistic Vehicle Health Management (VHM).

In this study, we investigate a deep learning-based vehicle fault prediction model aimed at achieving accurate prediction of vehicle faults by analyzing the correlations among different faults and the impact of critical faults on future fault development. To this end, we propose a collaborative modeling approach utilizing multiple attention mechanisms. This approach incorporates a graph attention mechanism for the fusion representation of fault correlation information and employs a novel learning method that combines a Long Short-Term Memory (LSTM) network with an attention mechanism to capture the impact of key faults. Based on experimental validation using real-world vehicle fault record data, the model significantly outperforms existing prediction models in terms of fault prediction accuracy.

Balise is a critical trackside component in railway systems, operating in harsh environments where silicone gel potting enhances reliability. However, the potting process is irreversible; defects lead to costly scrap and operational delays. For this reason, this study introduces an anomaly detection method for the transponder potting process for the first time. We propose an attention-enhanced autoencoder (AEA) for anomaly detection. Our approach integrates three core innovations: max-min normalisation to handle scaling discrepancies between input attributes, an attention mechanism that dynamically highlights key features, and empirically derived adaptive reconstruction error thresholds to discriminate anomalies. Experiments on real production data show that the AEA model achieves Precision of 97.10%, Recall of 100.00%, weighted F1-Score of 98.53%, Accuracy of 99.90%, ROC-AUC of 99.96%, and G-means of 98.54%, outperforming all baselines. Compared to the best baseline AE model, the AEA model improves Precision by 1.45%, Recall by 1.49%, weighted F1-Score by 1.47%, Accuracy by 0.09%, ROC-AUC by 0.03%, and G-means by 1.47%. These results demonstrated that our model significantly outperforms existing well-regarded models in anomaly detection performance.

Accurate prediction of vehicle maintenance demands is crucial for sustaining vehicle use, optimizing performance, and minimizing ownership costs. However, current methods only predict maintenance demand for specific vehicle components and lack the capability to offer a comprehensive prediction of all maintenance demands. Furthermore, predicting vehicle maintenance demand must incorporate the impacts of various essential maintenance projects on subsequent demands. To address these challenges, we propose an innovative method for predicting vehicle all maintenance demands based on collaborative multiview time series modeling. Leveraging the interdependencies among vehicle maintenance projects across various time periods, we employ a temporal dependency learning approach utilizing a multi-attention mechanism. To enhance the interaction between distinct time points and temporal dependencies, we developed a dependency-aware learning algorithm that effectively integrates and weighs the information and dependencies at each time step, thereby improving the model’s ability to capture the complex relationships among maintenance projects over time. To capture the significant impact of key maintenance projects on future demands, we propose a module that leverages both long short-term memory networks and the attention mechanism. Experimental results on actual vehicle maintenance records confirm that the proposed model outperforms existing methods, demonstrating its efficacy and applicability in predicting vehicle maintenance demand.

Accurate prediction of vehicle maintenance demands is crucial for enhancing service longevity and minimizing costs. However, current methods are limited to predicting maintenance demands for individual vehicle components. They fail to offer a comprehensive prediction that encompasses diverse maintenance demands. Additionally, vehicle maintenance demand prediction must consider the interrelationships among various maintenance projects and maintenance project records. To address these issues, we propose a vehicle maintenance demand prediction method that employs a collaborative approach. This method utilizes both static and dynamic correlation-aware learning techniques. We design a static correlation-aware method for maintenance project representation learning by leveraging prior statistical data from various maintenance projects. To effectively capture the temporal correlations inherent in different maintenance project records, we propose an attention-based dynamic correlation-aware technique. Experiments conducted on real-world datasets demonstrate that the proposed model outperforms existing methods.

Ensuring road safety is heavily reliant on the effective maintenance of vehicles. Accurate predictions of maintenance requirements can substantially reduce ownership costs for vehicle owners. Consequently, this field has attracted increasing attention from researchers in recent years. However, existing studies primarily focus on predicting a limited number of maintenance needs, predominantly based solely on vehicle mileage and driving time. This approach often falls short, as it does not comprehensively monitor the overall health condition of vehicles, thus posing potential safety risks. To address this issue, we propose a deep fusion network model that utilizes multi-source data, including vehicle maintenance record data and vehicle base information data, to provide comprehensive predictions for vehicle maintenance projects. To capture the relationships among various maintenance projects, we create a correlation representation using the maintenance project co-occurrence matrix. Furthermore, building on the correlation representation, we propose a deep fusion network that employs the attention mechanism to efficiently merge vehicle mileage and vehicle base information. Experiments conducted on real data demonstrate the superior performance of our proposed model relative to competitive baseline models in predicting vehicle maintenance projects.

It is of paramount importance to accurately predict the maintenance demands of vehicles in order to guarantee their sustainable use. Nevertheless, the current methodologies merely predict a partial aspect of a vehicle’s maintenance demands, rather than the comprehensive maintenance demands. Moreover, the process of predicting vehicle maintenance demands must give due consideration to the influence of mileage on such demands. In light of the aforementioned considerations, we put forth a vehicle overall maintenance demand prediction method that incorporates vehicle mileage awareness. In order to address the discrepancy between the vector space of mileage and that of the project, we put forth a mileage representation method for the maintenance demand prediction task. To capture the significant impact of key mileage and projects on future demand, we propose a learning module for key temporal information using a fusion of Long Short-Term Memory (LSTM) networks and attention mechanism. Moreover, to integrate maintenance mileage and projects, we propose a fusion method based on a gated unit. The experimental results obtained from real datasets demonstrate that the proposed model exhibits a superior performance compared to existing methods.