To address the problem of tribological failure in an aircraft piston pump valve plate pair, the friction and wear properties of the valve plate pair materials (W9Mo3Cr4V-HAl61-4-3-1) of an axial piston pump at a certain speed and load were studied using a disc-ring tester under lubrication with No. 15 aviation hydraulic oil. The results show that the friction coefficient (COF) fluctuated in the range of 0.019~0.120 when the load (L) increased from 30 N to 120 N, and the speed increased from 100 r/min to 500 r/min. With the increase in the rotational speed, the COF of the valve plate pair decreased first and then increased. When the rotation speed (V) was 300 r/min, the relative COF was the smallest. Under L lower than 60 N, abrasive wear was the main wear mechanism. Under L higher than 90 N, the main wear mechanism was adhesive wear but mild oxidation wear also occurred. In addition, based on the V, L, radius (R), and test duration (T), which affected COF, the random forest regression (RFR) algorithm, the bagging regression (BR) algorithm, and the extra trees regression (ETR) algorithm were used as machine learning methods to predict the COF of the valve plate pair. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) were used to evaluate its performance, with the results showing that the ETR prediction model was the best method for predicting COF. The results of the machine learning also showed that the contributions of V, L, R, and T were 43.56%, 36.76%, 13.13%, and 6.55%, respectively, indicating that V had the greatest influence on the COF of the W9Mo3Cr4V/HAl61-4-3-1 friction pair. This study is expected to provide support for the rapid development of new valve plate pair materials.

Frictional behaviors of nitrile rubber of screw pump stators with liquid nitrile rubber (LNBR) as a plasticizer with different mass portions(5,10,15,20) are investigated by the MPV-600 wear tester, and their physical properties and curing characteristics are studied. The morphologies of worn surface are measured by the scanning electron microscope. The results show that the tc90 time of the rubber is shortened due to LNBR modified stator nitrile rubber, and its processing performance is improved, hence energy is saved. The 100% modulus, tensile strength, elongation at break of stator nitrile rubber increases with the increasing amount of LNBR, while the tensile strength and hardness show the opposite trend, and LNBR is reactive plasticizer. It has a relatively excellent overall performance when the mass portion of LNBR is15.

In unmanned aerial vehicle inspection of transmission lines, insulators often exhibit arbitrary orientations and elongated shapes and are frequently embedded in complex backgrounds. Horizontal bounding boxes tend to include substantial redundant regions. Meanwhile, existing oriented object detection methods still suffer from angular discontinuities at periodic boundaries, insufficient geometric supervision for rotated bounding boxes, and difficulties in lightweight deployment. To address these issues, this paper proposes a lightweight oriented object detection model, termed R-YOLOv8-PSGH, which integrates dual-frequency phase-shift encoding and Gaussian geometric supervision. Based on a lightweight R-YOLOv8 architecture, a rotated detection head is developed to decouple the predictions of object categories, bounding-box locations, and orientation angles. To improve the periodic continuity of angle representations and strengthen the geometric constraints on rotated bounding boxes, a dual-frequency phase-shift angle encoding strategy and a Gaussian geometric localization loss are designed. Specifically, the complementary relationship between periodic signals with periods of 180°and 90° is exploited to map orientation angles into continuous phase responses, thereby improving the stability of orientation prediction. Moreover, the spatial structure of each rotated bounding box is modeled as a two-dimensional Gaussian distribution, and overlap consistency, center distance, and shape discrepancy are jointly optimized. In this manner, the orientation representation and bounding-box-level geometric supervision are collaboratively enhanced. Experimental results demonstrate that the proposed method improves the detection accuracy and localization stability of rotated objects while maintaining favorable lightweight deployment capability, providing a new solution for lightweight object detection in complex scenarios.

The vulcanizing properties, physical performances, and wear behaviors of nitrile rubber (NBR) filled with different kinds of Zinc Oxide (ZnO) were studied in this article. Based on samples preparation, this study provides the vulcanizing properties, physical performances and mechanical properties on NBR filled with different kinds of ZnO. This experimental results indicate that, compared with NBR filled with conventional ZnO, the tensile strength, tensile stress at 100%, and tensile stress at 300% of NBR filled with nano-ZnO are greater. The maximum torque of NBR filled with nano-ZnO is higher than that of NBR filled with conventional ZnO. The scorch time and optimum cure time of NBR filled with nano-ZnO are shortened. The NBR filled with nano-ZnO exhibits the more outstanding wear resistance.

Multi-source information fusion is regarded as a key approach for improving bearing fault diagnosis. However, due to the heterogeneity of multi-source information, asymmetric information contributions, and imbalanced discriminative features, negative transfer may occur during fusion. To address this issue, this paper proposes a negative-transfer-suppression diagnosis framework based on physical-information guidance and adversarially disentangled representation. First, an adaptive preprocessing mechanism guided by acoustic–vibration cross-correlation and mutual information entropy is constructed to extract intrinsic cross-modal correlations, enabling source-end feature reconstruction and commonality enhancement. Second, an attention-based spatial feature extraction operator and an adversarial common-domain representation model are developed to suppress modality-specific interference and disentangle cross-modal shared features. On this basis, sparse coding is employed to fuse common-domain and modality-specific features. Furthermore, a classification effectiveness evaluation index based on fuzzy clustering is introduced into the loss function to dynamically constrain sparse coding weights, thereby reducing interference features and suppressing negative transfer under strong-noise conditions. Experimental results demonstrate that the proposed method effectively achieves the design objective that “fusion outperforms non-fusion,” exhibiting strong noise robustness and high diagnostic accuracy under complex operating conditions.