Predicting the remaining useful life (RUL) of rolling bearings is a critical technology for ensuring safe operation and reducing the maintenance costs of rotating mechanical equipment. The accuracy of RUL prediction is highly dependent on the quality of degradation features screened from the original statistical features. However, there is redundancy between degradation features after screening, and the screening rule is highly subjective. Additionally, some useful information of the unselected features is lost. To solve the above problems, this paper proposes a feature fusion-based method for bearing RUL prediction. First, the self-organizing map (SOM) method is used to cluster the statistical features, which are then subjected to dimensionality reduction by the principal component analysis (PCA) method to obtain a fusion degradation feature set. Second, a BiLSTM network combined with the self-attention (SA) mechanism (BiLSTM-SA model) is established to predict bearing RUL with the fusion feature set. The SA mechanism is introduced to assign different weights to different fusion features to adaptively obtain the optimal feature combination. Finally, the effectiveness of the proposed feature fusion-based prediction method is verified on the FEMTO bearing dataset and IMS bearing dataset. The experimental results show that the proposed method can accurately predict bearing RUL, and its performance is superior to that of some state-of-the-art methods.

Green manufacturing has become a new production mode for the development and operation of modern and future manufacturing industries. The flexible job shop scheduling problem (FJSP), as one of the key core problems in the field of green manufacturing process planning, has become a hot topic and a difficult issue in manufacturing production research. In this paper, an improved multi-objective wolf pack algorithm (MOWPA) is proposed for solving a multi-objective flexible job shop scheduling problem with transportation constraints. Firstly, a multi-objective flexible job shop scheduling model with transportation constraints is established, which takes the maximum completion time and total energy consumption as the optimization objectives. Secondly, an improved wolf pack algorithm is proposed, which designs individual codes from two levels of process and machine. The precedence operation crossover (POX) operation is used to improve the intelligent behavior of wolves, and the optimal Pareto solution set is obtained by introducing non-dominated congestion ranking. Thirdly, the Pareto solution set is selected using the gray relational decision analysis method and analytic hierarchy process to obtain the optimal scheduling scheme. Finally, the proposed algorithm is compared with other algorithms through a variety of standard examples. The analysis results show that the improved multi-objective wolf pack algorithm is superior to other algorithms in terms of solving speed and convergence performance of the Pareto solution, which shows that the proposed algorithm has advantages when solving FJSPs.

This paper presents a new control methodology based on active disturbance rejection control (ADRC) for designing the tension decoupling controller of the unwinding system in a gravure printing machine. The dynamic coupling can be actively estimated and compensated in real time, which makes feedback control an ideal approach to designing the decoupling controller of the unwinding system. This feature is unique to ADRC. In this study, a nonlinear mathematical model is established according to the working principle of the unwinding system. A decoupling model is also constructed to determine the order and decoupling plant of the unwinding system. Based on the order and decoupling plant, an ADRC decoupling control methodology is designed to enhance the tension stability in the unwinding system. The effectiveness and capability of the proposed methodology are verified through simulation and experiments. The results show that the proposed strategy not only realises a decoupling control for the unwinding system but also has an effective antidisturbance capability and is robust.

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production.

High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll precision coating machine. However, the complex relationships in tension system make the problems of decoupling control difficult to be solved, which has limited the improvement of tension control accuracy for the coating machine. Therefore, an ADRC parameters self-tuning decoupling strategy based on RBF neural network is proposed to improve the control accuracy of tension system in this paper. Firstly, a global coupling nonlinear model of the tension system is established according to the composition of the coating machine, and the global coupling model is linearized based on the first-order Taylor formula. Secondly, according to the linear model of the tension system, a parameters self-tuning decoupling algorithm of the tension system is proposed by integrating feedforward control, ADRC and RBF. Finally, the simulation results show that the proposed tension control strategy has good decoupling control performance and effectively improves the tension control accuracy for the coating machine.

To address the issues of low trajectory planning efficiency, high motion impact, and poor operational stability in robotic arms during the automatic loading and unloading of aluminum blocks in coating machinery, a time-optimal trajectory optimization method based on a dynamically adaptive Particle Swarm Optimization (PSO) algorithm is proposed. First, the loading and unloading process of aluminum block components is described, followed by a kinematic analysis of the robotic arm in joint space. Then, the “3-5-3” hybrid polynomial interpolation method is used to fit the robotic arm’s motion trajectory and simulate the analysis. Finally, with the robotic arm’s operation time as the objective function, the dynamically adaptive PSO algorithm is applied to optimize the trajectory constructed by hybrid polynomial interpolation, achieving time-optimal trajectory planning for aluminum block handling. The results demonstrate that the proposed method successfully reduces the trajectory planning times for condition 1 and condition 2 from 6 s to 3.59 s and 3.14 s, respectively, improving overall efficiency by 40.2% and 47.7%. This confirms the feasibility of the method and significantly enhances the efficiency of automated loading and unloading tasks for aluminum blocks in coating machinery. The proposed method is highly adaptable and well-suited for real-time trajectory optimization of robotic arms. It can also be broadly applied to other robotic systems and manufacturing processes, enhancing operational efficiency and stability.