Search Results (4)

Auxiliary transport vehicles are essential components of the underground mine auxiliary transportation system, primarily used for tasks such as personnel and material transportation. However, the underground environment is complex, and unstructured roads exhibit significant randomness. Traditional passive hydropneumatic suspension systems struggle to strike a balance between ride comfort and stability, resulting in insufficient adaptability of auxiliary transport vehicles in such challenging underground conditions. To address this issue, this paper proposes a multi-mode hydropneumatic suspension control strategy based on the identification of road surface grades in underground mines. The strategy dynamically adjusts the controller’s parameters in real time according to the identified road surface grades, thereby enhancing vehicle adaptability in complex environments. First, the overall framework of the active suspension control system is constructed, and models of the hydropneumatic spring, vehicle dynamics, and road surface are developed. Then, a road surface grade identification method based on Long Short-Term Memory networks is proposed. Finally, a fuzzy-logic-based sliding mode controller is designed to dynamically map the road surface grade information to the controller’s parameters. Three control objectives are set for different road grades, and the multi-objective optimization of the sliding mode’s surface coefficients and fuzzy-logic-based rule parameters is performed using the Hiking Optimization Algorithm. This approach enables the adaptive adjustment of the suspension system under various road conditions. The simulations indicate that when contrasted with conventional inactive hydropneumatic suspensions, the proposed method reduces the sprung mass’s acceleration by 21.2%, 18.86%, and 17.44% on B-, D-, and F-grade roads, respectively, at a speed of 10 km/h. This significant reduction in the vibrational response validates the potential application of the proposed method in underground mine environments. Full article

Identifying bridge damage using a movable test vehicle is highly regarded for its mobility, cost-effectiveness, and broad monitoring coverage. Previous studies have shown that the residual contact-point (CP) response between connected vehicles is free of the impact of vehicle self-vibrations and road roughness, making it particularly suitable for the indirect extraction of bridge modal properties. However, most experimental campaigns regarding contact-point (CP) responses focus on a single-axle testing vehicle within a non-moving state. This study aims to theoretically and experimentally identify bridge damage using the instantaneous amplitude squared (IAS) extracted from the residual CP response of a two-axle passing vehicle. First, the closed-form solution of the residual CP acceleration was derived for a two-axle vehicle interacting with a simply supported beam. The IAS index was constructed from the driving frequency of the residual CP acceleration. Then, numerical investigations using finite element simulation were conducted to validate using the IAS index for indirect bridge damage identification. The application scope of the approach under various vehicle speeds and road roughness grades was examined. Finally, a laboratory vehicle–bridge interaction system was tested to validate the approach. Numerical studies demonstrated that bridge damage could be directly determined by observing the IAS abnormalities, which were baseline-free. The IAS from the residual CP response outperformed the IAS from CP responses in identifying bridge damage. However, it was better to use the IAS when the vehicle speed was no greater than 2 m/s and the grade of the road surface roughness was not high. Laboratory tests showed that it was possible to identify bridge damage using the IAS extracted from the residual CP acceleration under perfect road surfaces. However, it fell short under rough road surfaces. Hence, further experiments are required to fully examine the capacity of the IAS for bridge damage identification in practical applications. Full article

Aiming at the problems of poor overall vibration reduction and high energy consumption of in-wheel motor-driven electric vehicle (IWM-EV) active suspension on mixed pavement, a multi-mode switching control strategy based on pavement identification and particle swarm optimization is proposed. First, the whole vehicle dynamic model containing active energy-regenerative suspension and the reference model was established, and the sliding mode controller and PID controller designed, respectively, to suppress the vertical vibration of the vehicle and the in-wheel motor. Second, a road grade recognition model based on the dynamic travel signal of the suspension and the road grade coefficient was established to identify the road grade, and then the dynamic performance and energy-feedback characteristics of suspension were optimized by particle swarm optimization. According to the results of pavement identification, the optimal solution of the suspension controller parameters under each working mode was divided and selected to realize the switch of the suspension working mode. The simulation results show that the control strategy can accurately identify the grade of road surface under the condition of mixed road surface, and the ride index of the optimized active energy-regenerative suspension is obviously improved, while some energy is recovered. Full article

The objective of this study is to obtain a more detailed assessment of particles that contain rare-earth elements (REEs) in abandoned deposits of Brazilian fine coal tailings (BFCTs), so as to aid current coal mining industries in the identification of methodologies for extracting such elements (Santa Catarina State, Brazil). The BFCT areas were sampled for traditional mineralogical analysis by X-ray Diffraction, Raman Spectroscopy and nanomineralogy by a dual beam focused ion beam (FIB) coupled with field emission scanning electron microscopy (FE-SEM) and high-resolution transmission electron microscopy (HR-TEM) coupled with an energy dispersive X-ray microanalysis system (EDS). The results show that the smaller the sampled coal fines were, the higher the proportion of rare-earth elements they contained. Although the concentration of REEs is below what would normally be considered an economic grade, the fact that these deposits are already ground and close to the surface negate the need for mining (only uncovering). This makes it significantly easier for REEs to be extracted. In addition, owing to their proximity to road and rail transport in the regions under study, the opportunity exists for such resources (BFCTs) to be utilized as a secondary market as opposed to simply being discarded as has been done in the past. Full article