Search Results (5)

In the context of intelligent manufacturing, equipment fault early-warning technology has become a critical support for ensuring the continuity and safety of industrial production. However, with the increasing complexity of modern industrial equipment structures and the growing coupling of operational states, traditional fault warning models face significant challenges in feature recognition accuracy and adaptability. To address these issues, this study proposes a hybrid fault early-warning framework that integrates an improved bees algorithm (IBA) with a categorical boosting (CatBoost) model and kernel density estimation (KDE). The proposed framework first develops the IBA by integrating Latin Hypercube Sampling, a multi-perturbation neighborhood search strategy, and a dynamic scout bee adjustment strategy, which effectively overcomes the conventional bees algorithm (BA)’s tendency to fall into local optima. The IBA is then employed to achieve global optimization of CatBoost’s key hyperparameters. The optimized CatBoost model is subsequently used to predict equipment operational data. Finally, the KDE method is applied to the prediction residuals to determine fault thresholds. An empirical study on a deflection fault in the valve position sensor connecting rod of the mineral oil system in a gas compressor station shows that the proposed method can issue early-warning signals two hours in advance and outperforms existing advanced algorithms in key indicators such as root mean square error (RMSE), coefficient of determination (R²) and mean absolute percentage error (MAPE). Furthermore, ablation experiments verify the effectiveness of the strategies in IBA and their contribution to CatBoost hyperparameter optimization. The proposed method significantly improves the accuracy and reliability of fault prediction in complex industrial environments. Full article

For high-precision navigation, obtaining and maintaining high-precision point-of-interest (POI) data on the road network is crucial. In urban areas with complex terrains, the accuracy of traditional road network POI acquisition methods often falls short. To address this issue, we introduce DualNet-PoiD, a hybrid neural network designed for the efficient recognition of road network POIs in intricate urban environments. This method leverages multimodal sensory data, incorporating both vehicle trajectories and remote sensing imagery. Through an enhanced dual-attention dilated link network (DAD-LinkNet) based on ResNet18, the system extracts static geometric features of roads from remote sensing images. Concurrently, an improved gated recirculation unit (GRU) captures dynamic traffic characteristics implied by vehicle trajectories. The integration of a fully connected layer (FC) enables the high-precision identification of various POIs, including traffic light intersections, gas stations, parking lots, and tunnels. To validate the efficacy of DualNet-PoiD, we collected 500 remote sensing images and 50,000 taxi trajectory data samples covering road POIs in the central urban area of the mountainous city of Chongqing. Through comprehensive area comparison experiments, DualNet-PoiD demonstrated a high recognition accuracy of 91.30%, performing robustly even under conditions of complex occlusion. This confirms the network’s capability to significantly improve POI detection in challenging urban settings. Full article

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios. Full article

To reduce the cost of arranging air foam flooding equipment at each wellhead, a method of establishing centralized air foam flooding injection stations is proposed. The flow pattern and resistance characteristics of air foam flooding mixtures in different initial conditions are studied. Experimental results indicate that the probability density function of stratified flow is obtained by comparing stainless steel and transparent pipes. If the gas–liquid ratio is kept constant, then the shape of the probability density function remains unchanged in both stainless steel and transparent tubes. Meanwhile, the flow pattern under the gas–liquid ratio is determined by comparing the image recognition results with the probability density function, and a formula for calculating the resistance and pressure drop of the gas and liquid two-phase flow in the horizontal and upward pipes is established. Compared with the experiments, the error results of the calculation are small. Thus, the proposed equations can be used to predict the flow resistance of real air foam flooding. Full article

Considering the issue caused by the tail gas of viscous oil fire flooding, which carries a large amount of jeopardizing liquid, the Liaohe Oilfield No. 56 desulfurization station applies the vertical processing separator as the main dehumidification equipment for moisture elimination. However, the lack of study on the separator’s gas–liquid separation mechanism leads to unclear recognition of the equipment’s processing capability, which easily causes the desulfurization tower to water out, and the tail gas gathering network system to get frozen and blocked. To result in a solution to the problems above, numerical simulation software is applied in this paper based on the oil field’s actual operation data to establish a mathematical model for calculation, which may assist in simulating the gas–liquid separating process, in analyzing the flow field distribution within the separator, and in studying the dehumidification mechanism in terms of influencing factors and laws of equipment dehumidification efficiency. Finally, this helps optimizing the separator’s structure based on the calculation results. The research results provide a theoretical basis and technical support for the practical application of dehumidification equipment in oil fields. Full article