Aiming to assess the problems of low detection accuracy, poor reliability, and high cost of the manual inspection method for conveyor-belt-surface defect detection, in this paper we propose a new method of conveyor-belt-surface defect detection based on knowledge distillation. First, a data enhancement method combining GAN and copy–pasting strategies is proposed to expand the dataset to solve the problem of insufficient and difficult-to-obtain samples of conveyor-belt-surface defects. Then, the target detection network, the YOLOv5 model, is pruned to generate a mini-network. A knowledge distillation method for fine-grained feature simulation is used to distill the lightweight detection network YOLOv5n and the pruned mini-network YOLOv5n-slim. The experiments show that our method significantly reduced the number of parameters and the inference time of the model, and significantly improves the detection accuracy, up to 97.33% accuracy, in the detection of conveyor belt defects.

The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models have problems such as limited remote modeling capabilities and secondary computational complexity, it is difficult for existing deep learning anomaly detection methods to effectively exert their performance. The state space model (SSM) represented by Mamba is not only good at long-range modeling, but also maintains linear computational complexity. In this paper, using the state space model (SSM), we proposed a new visual state space reconstruction network (VSM-UNet) for the detection of CSC loosening anomalies. First, based on the structure of UNet, a visual state space block (VSS block) is introduced to capture extensive contextual information and multi-scale features, and an asymmetric encoder–decoder structure is constructed through patch merging operations and patch expanding operations. Secondly, the CBAM attention mechanism is introduced between the encoder–decoder structure to enhance the model’s ability to focus on key abnormal features. Finally, a stable abnormality score calculation module is designed using MLP to evaluate the degree of abnormality of components. The experiment shows that the VSM-UNet model, learning strategy and anomaly score calculation method proposed in this article are effective and reasonable, and have certain advantages. Specifically, the proposed method framework can achieve an AUROC of 0.986 and an FPS of 26.56 in the anomaly detection task of looseness on positioning clamp nuts, U-shaped hoop nuts, and cotton pins. Therefore, the method proposed in this article can be effectively applied to the detection of CSCs abnormalities.

The stator winding insulation system is the most critical and weak part of the EMU's (electric multiple unit) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent the fault from expanding into phase-to-phase or ground short-circuits. The TCU(traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is stationary. Three ITSC fault diagnostic conditions are based on different IGBTs control logic. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on random forest. The proposed method was validated using a simulation experimental platform for ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with the increase of ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location both in train and test samples are 100%.

Adjustable steam ejectors exhibit significant adaptability to various operating conditions. However, the coupling regulation mechanism between ejector performance and the internal flow field remains insufficiently understood, thereby limiting further optimization. The novelty of this study lies in elucidating the ejector’s performance regulation mechanism by examining the influence of spindle position on non-equilibrium condensation in wet steam. This approach clarifies the flow–thermal–phase-change coupling mechanism and interprets the resulting condensation suppression and shock wave dynamics. In this study, the effects of operating conditions and spindle position on ejector performance were quantitatively characterized. The flow-field evolution was further analyzed through key flow-field variables (pressure, Mach number, temperature, and condensate mass fraction). Moreover, the relationship between ejector performance and flow characteristics was investigated. The flow–thermal–phase-change coupling analysis reveals that the spindle effectively regulates steam ejector performance, internal thermodynamic behavior, and phase-transition processes by adjusting the equivalent throat diameter. Under a representative operating condition, compared with the baseline position (d_t = 5.66 mm), moving the spindle in the positive x-axis direction (to d_t = 5 mm) decreased the equivalent throat diameter and the motive-fluid mass flow rate by 11.7% and 22.6%, respectively. Consequently, the distance between adjacent shock waves gradually decreased along the flow direction (by approximately 14.1%), and the global maximum Mach number decreased sharply from 2.0 to 1.6 (a 20% reduction). The jet core was significantly shortened, while both the intensity and number of shock waves in the diffuser were reduced. Additionally, the local backflow near the wall of the mixing chamber’s contraction section was suppressed, resulting in a weaker temperature rise in the backflow region. The fluid temperature approached the outlet temperature more gradually, while the average flow-field temperature increased. Meanwhile, the condensate mass fraction in the mixing chamber was significantly reduced (from 0.1 to 0), and the entrainment ratio was enhanced. This configuration is suitable for applications requiring low discharge pressure, high motive pressure, or high suction pressure. Conversely, moving the spindle in the negative x-axis direction enlarged the equivalent throat diameter, which generated higher Mach numbers and stronger shock waves. This enlarged throat configuration enhances the ejector’s resistance to elevated discharge pressure and increases the critical discharge pressure, making it more suitable for high discharge pressure, low motive pressure, or low suction pressure conditions.