Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance.

In mineral engineering, harmful foreign objects mixed in the feed can cause faults in the sizer crusher (Sizer). The traditional feed identification methods are inefficient and manual, which fail to adapt to the present production conditions of high belt speed, thick material layer and large throughput. In previous studies, Sizer feed identification focused on single-component rather than mixed feeds. Therefore, we proposed a new Sizer mixed-feed identification method based on continuous wavelet transform (CWT) and squeeze-and-excitation residual network (SE-ResNet). Firstly, the three-channel sensor signals acquired during the Sizer feeding process were subjected to CWT, and the resulting scalograms were fused as the image dataset. Secondly, a Sizer feed identification model based on SE-ResNet was designed for single-component and mixed-feed classification. Ablation studies and method comparisons were conducted to validate the performance of the proposed model. Finally, the method of this paper was tested with industrial datasets to demonstrate its effectiveness and generalizability. The results show that the proposed method can achieve an accuracy of 85.99±0.16% for Sizer mixed-feed identification. This method also significantly improves the identification ability of wood, which solves the problem of distinguishing between coal and wood in the feed. Besides, industrial tests suggest that our method has good Sizer mixed-feed identification performance with an average accuracy of 95.64±1.77%, which can meet the demand of industrial production for the safe and smooth operation of the Sizer.

This article investigates the sliding wear behavior of ZTA (zirconia toughened alumina) particle (ZTAp)- and NbC (niobium carbide) particle (NbCp)-reinforced Fe60 matrix composites (ZTAp–NbCp/Fe60). ZTAp–NbCp/Fe60 was produced through vacuum sintering and tested on an M-2000 wear-testing machine. As revealed by the results, the wear loss increased with the increase in rotation speed and load. Abrasive wear occurred in the mild wear region; adhesive wear and serious abrasive occurred in the moderate wear region; and fatigue wear and ZTAp broken occurred in the severe wear region. According to the aforementioned conclusions, a more clean and intuitive wear mechanism diagram was established. In the process of wear, ZTAp was well combined with the matrix, and there was no crack at the interface. Meanwhile, in the wear process, ZTAp could block furrow, weaken the wear of the micro-convex ring, and improve the sliding wear performance of the composite material.

Addressing the challenges of heavy load disturbances, inter-cylinder coupling, and limited computational resources in the dual-cylinder electro-hydraulic reverse synchronization system of shuttle car steering mechanisms in shortwall coal mining, this paper proposes a position-force controller based on lumped disturbance observation (LDO-DPFC). First, leveraging the rigid connection characteristics of the dual cylinders, the traditional multi-state modeling is simplified to a single-state over-actuated system, thereby reducing system complexity. A lumped disturbance observer (LDO) is designed that analyzes the implicit relationship between acceleration and lumped disturbances, and combines the Levant differentiator to obtain velocity and acceleration estimates, achieving online estimation of lumped disturbances including friction and load forces. Building upon this foundation, a robust position controller without damping feedback is proposed, which exploits the system's inherent strong damping characteristics to avoid oscillations. A force distribution controller is designed based on the disturbance observation system, enabling free allocation of dual-cylinder thrust and elimination of coupling forces. Through cascaded system stability analysis, the uniform ultimate boundedness of the closed-loop system is proven. Simulation and experimental results demonstrate that the proposed controller achieves position tracking errors less than 2 mm and inter-cylinder coupling forces below 300 N, with performance comparable to the adaptive synchronization controller used as a control group, while requiring only 4 tunable parameters and extremely low computational resource consumption, thus possessing significant engineering application value.