Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search with hybrid A* and reeds-shepp curve to address the above limitations. By rapidly identifying the optimal initial parking pose, the proposed method ensures the kinematic feasibility and smoothness of the resulting trajectories. To further improve efficiency and safety in tight spaces, a hybrid collision detection mechanism is developed by combining a rectangular envelope with multi-circle fitting. The hierarchical geometric modeling approach significantly reduces computational cost while maintaining high detection accuracy. The method is validated through both simulations and real-vehicle experiments in vertical and parallel parking scenarios. Results demonstrate that in typical constrained scenarios, the average planning time is only 0.543 s, and the number of direction changes is maintained between 1 and 6, demonstrating superior computational efficiency and improved trajectory smoothness. These attributes make the algorithm highly suitable for practical deployment in advanced driver assistance systems and autonomous vehicles.

Rocker–bogie suspension systems have been extensively employed in planetary exploration rovers due to their ability to traverse highly irregular terrains while maintaining ground contact. Traditionally, their mechanical behavior has been analyzed using quasi-static models, given the low operational speeds typical of space missions. However, similar configurations are now being proposed for terrestrial applications in agriculture, defense, and logistics, where higher traversal speeds and more varied terrain conditions require a deeper understanding of the system’s dynamic response. This study analyzes some aspects of the kinematic and dynamic behavior of a rover with rocker–bogie suspension while traversing an obstacle with a harmonic profile. Both quasi-static and dynamic simulations are conducted, focusing on the time-varying contact forces at the wheels. Key findings include identifying the rate at which load reduction at which the load on one wheel becomes zero and the wheel tends to lift off the ground. These threshold speeds are mapped as a function of height and wavelength of the bump, providing design insights for applications requiring higher traversal speeds on uneven terrain. The analysis may also prove valuable for rovers equipped with visual sensor systems capable of mapping their surroundings and identifying obstacles, to determine whether they can be traversed and, if so, at what maximum speed. An experimental investigation was conducted with a small-scale rover to verify the theoretical results, for which the threshold speed was found to be 0.3 m/s, calculated for h = 16 mm and λ = 80 mm.

Thermally induced deformation is a major source of dimensional error in end-milling, especially under high-speed or high-load conditions. Direct measurement of workpiece deformation during machining is impractical, while temperature signals can be obtained with good stability using embedded thermocouples. This study proposes an indirect method for predicting milling-induced thermal deformation based on temperature measurements. A three-dimensional thermo-mechanical finite element model is established to simulate the transient temperature field and corresponding deformation of the workpiece during milling. The numerical model is validated using cutting experiments performed under the same boundary conditions and machining parameters. Based on the validated results, the relationship between deformation at critical machining locations and temperature responses at candidate monitoring points is analyzed. To improve applicability to complex workpieces, a statistical prediction model is developed. Temperature monitoring points are optimized, and significant temperature–deformation correlations are identified using multiple linear regression combined with information-criterion-based model selection. The final model is constructed using simulation-derived datasets and provides stable deformation prediction over the entire milling process.

Bearings are important elements of mechanical systems and the correct forecasting of their remaining useful life (RUL) is key to successful predictive maintenance. Nevertheless, noise interference during different operating conditions is also a significant problem in predicting their RUL. Existing denoising-based RUL prediction models often show degraded performance when exposed to heterogeneous and non-stationary noise, resulting in unstable feature extraction and reduced generalisation. To address the challenge of heterogeneous and non-stationary noise in bearing RUL prediction, this study proposes a hybrid framework that combines a noise-conditioned convolutional denoising autoencoder (NC-CDAE) and a temporal attention transformer (TAT). The NC-CDAE adaptively suppresses diverse noise types through conditional modulation, while the TAT captures long-term temporal dependencies to enhance degradation trend learning. This synergistic design improves both the noise robustness and temporal modelling capability of the system. To further validate the model under varying conditions, synthetic datasets with different noise intensities were generated using a conditional generative adversarial network (cGAN). Comprehensive experiments show that the proposed NC-CDAE + TAT framework achieves lower and more stable errors than state-of-the-art methods, reducing RMSE by up to 23.6% and MAE by 18.2% on average and maintaining consistent performance (an RMSE between 0.155 and 0.194) across diverse conditions.

As a critical part of hoisting equipment in mines, the damage of mining wire rope (MWR) directly poses a threat to production safety and economic efficiency within the coal industry. Therefore, developing efficient and reliable methods for MWR damage identification is of great significance. However, acquiring substantial labeled damage data in real industrial scenarios is often impractical, and accurate damage identification with limited labeled data remains an urgent challenge. To address this, this paper proposes a self-supervised contrastive learning method based on multi-scale convolution and Informer (MS-Informer) for the quantitative damage identification of MWR. This method first generates positive and negative sample pairs through a combined data augmentation strategy to increase data diversity and enhance feature generalization. Subsequently, multi-scale convolution is combined with Informer to simultaneously capture local details and global features. Finally, limited labeled data are incorporated during the fine-tuning phase to accurately identify MWR damage. The experimental results on multiple MWR damage datasets demonstrate that the proposed method achieves superior identification performance under conditions of limited labeled data. This framework not only enhances the accuracy and reliability of MWR damage detection but also provides crucial guidance for the safe operation and subsequent maintenance of mine hoisting equipment.

Aramid fiber is increasingly regarded as an important skeleton material in the conveyor belt industry. However, its application is limited by problems such as short splice service life and low strength retention. This study investigates the finger splice of an aramid rubber conveyor belt. A finite element model was established to analyze the effects of different rubber hardness values (60, 65, 70, 75), environmental temperatures (−20 to 40 °C), and finger widths (10 mm, 12 mm, 15 mm, 20 mm, 30 mm) on splice mechanical performance. The results show that the maximum stress is concentrated at the end surfaces of the reinforcement layer and the fingertips of the skeleton material. The splice strength increases with higher rubber hardness but decreases with rising environmental temperature. To improve splice strength, the rubber hardness and environmental temperature should be controlled within 60–70 and 0–20 °C, respectively. Although reducing finger width increases splice strength, excessively small widths lead to stress concentration and manufacturing difficulties. Therefore, finger width selection should consider actual working conditions. The experimental results are compared with simulation results, and the trend consistency verifies the correctness of the simulation. This study provides a theoretical basis for parameter selection and structural design of the splice under various working conditions.

This paper addresses the insufficient speed control accuracy observed in traditional seeding systems. This paper proposes an electric drive seeding control method that incorporates a composite control strategy combining the Rime optimization algorithm (RIME) with a backpropagation neural network (BPNN). Firstly, the architecture including radar/proximity switch dual-mode speed measurement, STM32F103 main control, and asymmetric half-bridge drive was constructed. Based on the kinematic model, a motor speed-plant spacing mapping relationship was derived to complete the selection of a brushless DC motor. Secondly, this study addresses the issues of large overshoot in traditional PID control, response lag in fuzzy PID, and local optima in BP-PID. To overcome these challenges, the RIME algorithm is employed to optimize the weight-updating mechanism of the backpropagation neural network (BPNN). The soft RIME search facilitates multi-directional exploration, while the hard RIME puncture enhances global optimization capability, significantly improving the adaptive accuracy of the parameters. The simulation results showed that the adjustment time of the proposed RIME-BP-PID in the step response is 73.8% shorter than the BP-PID, and the overshoot is reduced to 0.23%. The square wave tracking error is 27.8% of the traditional PID. The bench test was carried out at 6–12 km/h speed and 200–300 mm. The results showed that, compared with BP-PID, the qualified index of RIME-BP-PID increased by 1.67–1.94 percentage points, the missed seeding index decreased by 1.25–1.80 percentage points, and the coefficient of variation decreased by 4.90–5.82 percentage points. The algorithm effectively solves the problem of the strong nonlinear time-varying control of a seeding system and provides theoretical support for the research and development of precision agricultural equipment.

Vertical axial-flow pumps with bidirectional passages are widely used in applications requiring flow reversal. However, their unique inlet geometry often leads to asymmetric impeller inflow conditions. This study investigates the internal flow behavior and pressure pulsation characteristics of a vertical bidirectional axial-flow pump under design, critical stall, and deep stall conditions using unsteady Reynolds-averaged Navier–Stokes simulations combined with Fast Fourier Transform and wavelet analysis. Results show that the pump reaches peak efficiency at the design point, with critical and deep stall occurring at 0.6 Qdes and 0.5 Qdes, respectively. The head at the deep stall condition shows a further drop of 7.51% compared to the critical stall condition. This progressive performance degradation is attributed to vortex-induced blockage: it initiates with the intensification of the tip leakage vortex and evolves into large-scale separation vortices covering the suction surface under deep stall—a mechanism distinctly influenced by the bidirectional inlet’s stagnant water zone. Inlet asymmetry, reflected by a normalized velocity coefficient (Vn) below 0.6 in the stagnant water zone under design flow, is partially mitigated during stall due to flow confinement. Pressure pulsations at the blade leading edge are dominated by the blade passing frequency (BPF), with amplitudes under critical stall about 3.2 times those at design conditions. At the impeller outlet, critical stall produces a mixed dominant frequency (shaft frequency and BPF), whereas deep stall yields the highest pulsation amplitude (BPF ≈ 4.8 × the design value) resulting from extreme passage blockage. These findings clarify how bidirectional-inlet-induced vortices modulate stall progression and provide theoretical guidance for enhancing the operational stability of such pumps under off-design conditions.

Overhead transmission lines often suffer from mutual collisions between adjacent conductors in windy weather, which can cause power failures to villages. To solve this problem, this paper introduces a spacer ring and a teleoperated robot for the installation and retrieval of the ring. The spacer ring and robot address the installation challenges of the anti-collision devices and enhance transmission line maintenance. Fixed by the locking mechanism, the spacer ring can isolate adjacent conductors to avoid collisions. The structure and working principle of the spacer ring and installation robot are introduced. Static analysis and finite element analysis (FEA) are conducted to analyze the output force of the locking mechanism, which is then validated through experiments. Experimental results show that the locking mechanism can generate a strong output force of up to 2000 N with about 6.0 N·m of input torque, providing a secure installation for the spacer ring. Diverse installation tests have validated the robot’s capability for live-line operations on transmission lines. Field tests indicate that the installation robot can travel at 0.3 m/s on a 15° slope and successfully install the spacer rings.

Triboelectric nanogenerators (TENGs) offer intrinsically high open-circuit voltages in the kilovolt range; however, conventional diode rectifier interfaces clamp the voltage prematurely, restricting access to the high-energy portion of the mechanical cycle and preventing delivery-centric control. This work develops a unified physical basis for contact–separation (CS) TENGs by confirming the consistency of the canonical Voc–Cs relation with a dual-capacitor energy model and analytically establishing that both terminal voltage and storable electrostatic energy peak near maximum plate separation. Leveraging this insight, a self-powered gas-discharge-tube (GDT) rectifier bridge is devised to replace two diodes and autonomously trigger conduction exclusively in the high-voltage window without auxiliary bias. An inductive buffer regulates the current slew rate and reduces I2R loss, while the proposed topology realizes two decoupled power rails from a single CS-TENG, enabling simultaneous sensing/processing and actuation. A low-power microcontroller is powered from one rail through an energy-harvesting module and executes an operator-based nonlinear controller to regulate the actuator-side rail via a MOSFET–resistor path. Experimental results demonstrate earlier and higher-efficiency energy transfer compared with a diode-bridge baseline, robust dual-rail decoupling under dynamic loading, and accurate closed-loop voltage tracking with negligible computational and energy overhead. These findings confirm the practicality of the proposed self-powered architecture and highlight the feasibility of integrating operator-theoretic control into TENG-driven rectifier interfaces, advancing delivery-oriented power extraction from high-voltage TENG sources.