# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 1 Introduction  
Intelligent manufacturing is the engine driving the development of future industry. With the rapid development of communication technology and information technology [1], based on cloud computing, big data, and the Internet of Things [2], a new round of industrial transformation represented by intelligent manufacturing has begun globally. Digital twin (DT) technology supports the core technology system of intelligent manufacturing, promotes technology integration, and is considered to be the key to realizing intelligent manufacturing. Digital twin technology no longer refers to three-dimensional static models, but refers to the use of physical models, sensor updates, operating history, and other data to map physical entities to virtual space, so as to reflect the whole life cycle process of the corresponding physical entities and provide a reference for system analysis and decision-making [3]. A digital twin model will continuously collect real-time data from the physical production line and use real-time data and historical data for model training, model verification, model updating, and feedback of the final results to the physical production line to achieve production control. The physical production line will be optimized based on the results of the virtual production line (such as the fault diagnosis results in this paper). With the development of science and technology, the intelligent production line is developing towards a high degree of automation and integration. As the production process becomes increasingly complex, the possibility of related failures in the entire system will increase during the continuous operation of the production line. Minor failures in theproduction process may cause irreparable losses. At the same time, with the introduction of more large-scale mechanized equipment in the production line, the equipment has a production order relationship. As a part of the production line, the stability and safety of a single piece of equipment directly affects the continuous operation of the whole production line [4]. With the development of machine learning technology, the acquisition of knowledge is not completely dependent on expert experience, but uses some intelligent methods based on data. The data-based fault diagnosis method has strong practicability and universality. Data-based intelligent methods can deeply mine high-dimensional data and extract high-value information hidden in the data. This high-value information can reflect the essence of the object. Therefore, we can establish the corresponding fault diagnosis model to effectively complete various fault diagnosis tasks. By using the data generated in the production line to establish a model, it is possible to judge the running state of the equipment in real time. Mining the valuable information behind the data is why data-driven fault detection methods have attracted widespread attention in recent years. There are many classic algorithms in this field that significantly improve the efficiency and accuracy of fault diagnosis, such as Bayesian networks [5], support vector machines (SVMs) [6], artificial neural networks (ANNs) [7], decision trees [8], etc. [9; 10; 11]. [PERSON] et al. proved that random forest (RF) has a higher accuracy rate than ANNs and SVMs, and so it is used in machine fault diagnosis [12]. In recent years, an increasing number of scholars have used machine learning methods for multi-class fault classification. For example, [PERSON] et al. [13] proposes a hybrid algorithm based on binary ant colony (BACO) and SVM. The BACO algorithm is used to select the appropriate feature subset and optimize the parameters of the SVM to improve the classification accuracy. [PERSON] et al. [14] proposed a method of combining spark and improved random forest to solve the problem of fault diagnosis. By eliminating decision trees with lower accuracy, a decision tree with higher accuracy and faster accuracy can be established. Then, a series of experiments can verify the effectiveness of the algorithm. [PERSON] et al.