关键词: 数字孪生, 智能纺纱, 故障预测与健康管理, 增强现实, 多维度建模, 智能工厂

Abstract: Intelligent spinning, as a key path for the modernization and automation evolution of the textile industry, aims to improve the efficiency, quality, and flexibility of the spinning process by integrating advanced automation technology, information technology, and intelligent control systems. Its development has undergone multiple critical stages from mechanical automation to intelligent control. Initially, innovations in devices such as ring spinning machines and air-jet spinning machines achieved operational automation, significantly boosting production efficiency. Subsequently, the introduction of computer control technology marked the phase of semi-automation and localized intelligence. Entering the stage of intelligent control, the application of information technology, sensor technology, and artificial intelligence enabled real-time monitoring, data analysis, and automatic optimization of the spinning process, driving the industry towards high efficiency, stability, and intelligence.
Digital twin technology has become a revolutionary concept since it was first introduced by Michael Grieves in his Product Lifecycle Management (PLM) course in 2002. The core of this technology lies in building a virtual model of physical entities to achieve real-time monitoring, simulation, and optimization of physical entities. With the advent of Industry 4.0, digital twin technology has been widely applied in various industries, including energy, healthcare, urban management, etc. Its application scenarios have expanded from a single device to the entire production line and even the entire supply chain. The combination of data analysis and artificial intelligence technology enables digital twin systems to perform more complex simulations and predictions.
In the context of the development of intelligent spinning, the introduction of digital twin technology has brought new opportunities and transformations. This paper explores the implementation strategies of digital twin technology in intelligent spinning, including the architecture and core technical components of the digital twin system in spinning factories. Specifically, it delineates the components of geometric, logical, and decision models in multi-dimensional modeling, along with specific modeling steps. In terms of equipment fault prediction and health management, the paper emphasizes the advantages of integrating digital twin technology with Prognostics and Health Management (PHM) systems, and analyzes methods for achieving consistency between virtual and real data and iterative optimization of models. Furthermore, the integration of Augmented Reality (AR) visualization management systems with digital twin technology creates advanced data presentation methods, detailing the implementation of key technologies such as tracking registration, virtual-real fusion, and human-machine interaction in AR systems.
The deep integration of digital twin technology and spinning will bring more efficient, intelligent, and sustainable production methods to the textile industry. In the future, the application of digital twin technology will focus on enhancing precise modeling, refining mechanistic models, improving the accuracy of predictive maintenance, and strengthening data interaction security, to continuously advance its implementation in the spinning field.

Key words: digital twin, intelligent spinning, prognostics and health management, augmented reality, multi-dimensional modeling, intelligent factory