# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 1 Introduction
[15] used the unsupervised training of a denoising autoencoder to obtain a deep neural network for fault diagnosis. [PERSON] et al. [16] used a genetic algorithm and particle swarm optimization algorithm to optimize the super parameters of an SVM and applied them to the fault diagnosis of a centrifugal valve. The above studies have proven the effectiveness of machine learning methods for fault diagnosis. Ensemble learning is a method of machine learning, which uses multiple models to solve the same problem. The core idea of ensemble learning is to build multiple models, and then fuse the decision results of each model to get better results than a single model. By combining multiple models, the error of a single model may be compensated by other models, so the overall performance of the ensemble is better than that of a single model [17]. In an actual fault diagnosis experiment on an elevator motor system, [PERSON] et al. [18] adopted four algorithms fusing majority voting, which verifies the feasibility of the algorithm through an experiment and proves the great potential of ensemble learning in practical applications. Random forest [19] is one of the most classic algorithms in ensemble learning. Random forest [19] is one of the most classic and most popular algorithms in ensemble learning [17]. Random forest has been used in various fields since it was first proposed. The random forest algorithm has a simple structure, superior performance, and strong interpretability. Compared with other machine learning methods, it performs better in many classification and regression tasks [20]. Due to the randomness, random forest is not easy to overfit; it has good antinoise ability and is not sensitive to outliers or abnormal points. [PERSON] et al. studied the possibility of applying the random forest (RF) algorithm to machine fault diagnosis and proposed a hybrid method combined with a genetic algorithm to improve the accuracy of the classification [12]. However, traditional ensemble learning also has disadvantages. [PERSON] et al. [21; 22] have found that there are differences among the classifiers in ensemble learning, and the results of some classifiers will reduce the overall accuracy. Using the selective integration method, it is better to select a good classifier than selecting all classifiers. For example,Zhou et al. [21; 22] proved that it may be better to ensemble many instead of all of the neural networks and decision trees from the point of view of regression and classification, and proposed an algorithm called GASEN and GASEN-b, which uses a genetic algorithm to improve weights. [PERSON] et al. [23] proposed a multi-objective instance weight transfer learning network to solve the problem of fault diagnosis. The effectiveness of the proposed algorithm was verified by industrial robot and spot-welding experiments. Most traditional machine learning algorithms used for classification must satisfy two preconditions to perform well [24]: (1) the training dataset contains balanced information; (2) the training dataset and the test dataset satisfy the same feature distribution. These conditions are difficult to meet in the actual production. In the actual operation of the production line, most of the data are obtained under normal conditions, so fault data are difficult to obtain, and the loss of the production line is irreparable. In addition, if the production conditions change, the training dataset and the test dataset do not satisfy the same feature distribution. The accuracy of the model will decline sharply, so a model trained in a previous stage cannot be used in the current stage. The cost of retraining the model will increase [25]. Therefore, in this paper, we propose a method combining digital twin with an improved random forest algorithm for fault diagnosis on an intelligent production line.