Job shop scheduling always plays an important role in the manufacturing process and is one of the decisive factors influencing manufacturing efficiency. In the actual process of production scheduling, there exist some uncertain events, information asymmetry and abnormal disturbance, which would cause the execution deviation and affect the efficiency and quality of scheduling execution. Traditional scheduling methods are not sufficient to solve the challenges well. Due to the rise of digital twin, which has the characters of virtual reality interaction, real time mapping and symbiotic evolution, a new job shop scheduling method based on digital twin is proposed to reduce the scheduling deviation. In this paper, the architecture and working principle of the new job shop scheduling mode are introduced. Then, scheduling resource parameter updating methods and dynamic interactive scheduling strategies are proposed to achieve real-time and precise scheduling. Finally, a prototype system is designed to verify the validity of this new job shop scheduling mode.

Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices.

As a key equipment of manufacturing, the intelligence of CNC machine tools affects the development and progress of intelligent manufacturing. At the same time, the rapid development of various technologies in recent years, such as cloud computing and edge computing, has brought new methods to improve the intelligence of CNC machine tools. This paper proposes a new architecture of intelligent machine tools based on edge-cloud collaboration (IMT-ECC). The hierarchical structure of IMT-ECC is introduced and consists of three layers, data acquisition, network communication and edge-cloud collaboration. Combining the real-time characteristic of edge computing and complex problem processing ability of cloud computing, the edge-cloud collaboration is designed to improve the intelligence of machine tools through data collaboration, information collaboration and knowledge collaboration. Finally, the feasibility of the new intelligent machine tool architecture based on edge-cloud collaboration is verified by experiments with gantry heavy-duty CNC machine tools.

The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.