To accurately estimate the remaining useful life of rolling bearings, which are essential components in mechanical devices, would greatly enhance their reliability and efficiency. A novel prediction approach that integrates digital twin technology with time series forecasting, employing the Informer model, is introduced. By utilizing digital twin technology to establish a virtual model of the bearing, we can monitor and simulate the physical condition of the bearing. Then the Informer is utilized to complete the remaining useful life projection on the information provided by the processed digital twin technology. The experiment demonstrates that the method can accurately estimate the remaining useful life of the bearing, which provides strong support for realizing intelligent maintenance and fault prevention of equipment.
Aiming at the problem that the traditional ORB algorithm is difficult to realize the real-time and accurate grasp of the object by the robotic arm equipped with the binocular stereo vision,a robotic arm rec-ognition and grasping method based on an improved ORB algorithm was proposed.Firstly,the improved ORB al-gorithm was used to identify the target,and then the target was positioned according to the binocular stereo vi-sion optical axis parallel model.Then,the mathematical model of the robotic arm was established by using the standard D-H parameter method,and the inverse kinematics was solved.Finally,the obtained drive angles of each joint of the robotic arm were transmitted to the control end of the system.The end effector of the robotic arm was driven to complete the grasp of the target.The results show that compared with the traditional ORB algo-rithm,the improved ORB algorithm in this study can improve the speed and accuracy of target recognition and positioning,and effectively improve the real-time and accurate grasping of the robotic arm.
In order to solve the problem that the current tool wear defects are difficult to be collected by the visual inspection system, an Otsu threshold segmentation algorithm based on particle swarm optimization is proposed.to detect tool wear The algorithm improved update strategy for inertia coefficients which effectively expanding the search scope of the algorithm and shortens the running time of the algorithm. By adding a perturbation equation to the particle swarm, solved the problem of traditional particle swarm optimization algorithms easily falling into local optima. Finally, an experimental platform is built to verify the effectiveness of the algorithm. This inspection method can achieve the identification of tool damage areas and the measurement of tool damage amount, and has advantages such as high recognition accuracy and fast running speed compared to traditional Otsu algorithm, Canny algorithm ,local threshold segmentation and so on. The research results have certain reference value for the actual tool defect detection system.
The proposed method for tire specification character recognition based on the YOLOv5 network aimed to address the low efficiency and accuracy of the current character recognition methods. The approach involved making three major modifications to the YOLOv5 network to improve its generalization ability, computation speed, and optimization. The first modification involved changing the coupled head in YOLOv5 to a decoupled head, which could improve the network's generalization ability. The second modification proposed incorporating the C3-Faster module, which would replace some of the C3 modules in YOLOv5's backbone and head and improve the network's computation speed. Finally, the third modification proposed replacing YOLOv5's CIoU loss function with the WIoU loss function to optimize the network. Comparative experiments were conducted to validate the effectiveness of the proposed modifications. The C3-Faster module and the WIoU loss function were found to be effective, reducing the training time of the improved network and increasing the mAP by 3.7 percentage points in the ablation experiment. The experimental results demonstrated the effectiveness of the proposed method in improving the accuracy of tire specification character recognition and meeting practical application requirements. Overall, the proposed method showed promising results for improving the efficiency and accuracy of automotive tire specification character recognition, which has potential applications in various industries, including automotive manufacturing and tire production.
In order to recognize the fault type of reducer abnormal vibration and reduce the cost of inspection and maintenance, an intelligent diagnosis model is developed. In the case of insufficient historical abnormal vibration data, a fault tree of the reducer is established by combing the historical fault data. It is then mapped to the Bayesian network structure. The expectation maximization (EM) algorithm is selected as the parameter learning method to determine the probability distribution of the node variables. After processing real-time vibration data, the model integrates the abnormal vibration feature discrimination mechanism and hierarchical Gibbs sampling algorithm to carry out fault probability inference. Compared with other models, the proposed model has achieved great improvement in the accuracy of diagnosis results and distinguishing normal and abnormal data. The model is integrated into the intelligent operation and maintenance system of belt conveyor for engineering verification.
