Multi-sensor fault diagnosis, especially when using heterogeneous sensors, substantially enhances the accuracy of fault detection in asynchronous motors operating under high-interference conditions. A critical challenge in multi-sensor fault diagnosis lies in effectively fusing data from different sensors. Deep learning offers a promising solution by transforming multi-sensor data into a unified representation, thereby facilitating robust data fusion. However, existing approaches often fail to fully exploit inter-sensor correlations and inherent prior physical knowledge. To address this limitation, we propose a novel graph neural network-based model that emphasizes graph structure construction for heterogeneous multi-sensor information fusion. Our framework includes (1) a multi-task enhanced autoencoder for node feature extraction, enabling discriminative representation learning, particularly with heterogeneous sensor data; (2) an adjacency matrix builder integrated with physical prior constraints to improve the generalization and robustness of the model; and (3) a graph isomorphism network to derive graph-level representations for fault classification. Our experimental results demonstrate the model’s effectiveness in diagnosing faults, as it achieves superior performance compared to conventional methods on two heterogeneous asynchronous motor datasets.

Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper proposes a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive capabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Winters model.

A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city’s transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify travel endpoints based on the characteristics of trajectory location information. It uses Shenzhen cellular signaling data to visually analyze the spatial and temporal distribution of urban traffic demand, traffic correlation, and asymmetry of traffic flow between different traffic zones. The results confirm that mobile internet information represented by cellular signaling information can effectively reflect the traffic status of urban areas, which, compared to traditional travel survey methods, has the advantages of lower cost, more timely feedback, and can be durably carried out in the long term.

Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if the aggregation size is too large, the data information will be lost, resulting in the forecast results tending towards an average or slow response. With the development of intelligent transportation systems, especially the development of urban traffic control systems, high requirements are placed on the real-time accuracy of traffic forecasting. Therefore, it is an essential topic of traffic forecasting research to determine aggregation sizes. In this paper, the mutual information between the forecast input information and the forecast result and the sequence complexity of the forecast result measured by approximate entropy, sample entropy, and fuzzy entropy are considered; then, the optimal data aggregation size is given. To verify the proposed method, the validated data obtained from the simulation is aggregated and calculated with different aggregation sizes, then used for forecasting. By comparing the prediction performance of different aggregate sizes, the optimal aggregate size was found to reduce MSE by 14–30%. The results show that the method proposed in this paper is helpful for selecting the optimal data aggregation size in forecasting and can improve the performance of prediction.

Accurate Remaining Useful Life (RUL) prediction for bearings is essential for machinery safety and widely applied across industries. Data-driven RUL prediction became the mainstream for RUL prediction, owing to their effectiveness and broad applicability. However, Data-driven RUL prediction confronts the difficulties of modeling complex vibration signal patterns and the scarcity of bearing full lifecycle data. To address these issues, this paper proposes a novel framework that integrates a dual-path GNN with a progressive fault-data-driven transfer learning strategy. First, a dual-path GNN architecture is designed to process separately constructed temporal-causal and spectral-interaction graphs, followed by a late fusion mechanism to preserve domain-specific patterns. The temporal graph is built using Transfer Entropy to infer directed causal relationships. Second, a twostage progressive transfer learning framework is introduced to bridge the domain gap between abundantly available fault data (source) and limited run-to-failure data (target), enhancing RUL prediction accuracy in data-scarce scenario. Comprehensive experiments demonstrate that the proposed method achieves superior accuracy and robustness in RUL prediction compared to state-of-the-art approaches, particularly under data-scarce conditions.