# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 3 Digital-Twin-Assisted Fault Diagnosis Method Using Transfer Learning
### Proposed DT + IRF Method  
In an actual factory, the DT + IRF method we propose can be summarized by the following process. First, engineers can use the manufacturing platform to build virtual models corresponding to physical entities. A high-fidelity virtual model is the key point. It includes a geometric model, a function model, and an information model [38]. (1) The geometric model maps the physical entities of the real world. It is used to express the relationship attributes and logic of digital twins. The topological model can be used to describe the kinematic relationship. (2) The function model is used to represent the behavior and function in the virtual model. These include data acquisition, synchronization, and storage, as well as simulation functions and fault diagnosis. (3) The information model connects the physical entity and the virtual model. The data collected by different devices are heterogeneous. This will cause difficulties in data connection. The meaning of various information will be defined in the information model.  
When the digital twin scene is built, real-time mapping can be generated with physical entities. We started by training the fault diagnosis model. The overall framework of the proposed algorithm in this paper is shown in Algorithm 1. In the digital twin scenario, the data are obtained by adjusting the parameters generated by the physical entity and the simulation cases in different states, and the corresponding tags are manually added. Then we use the algorithm to train the model. First, the training data are divided into a large number of simulated data and a small number of real data. Among them, the simulated data are balanced, but the actual data lack fault data. The test set is composed of data from the physical production line. In the training model stage, a large number of simulation data are divided into a training set (\(X_{v\_train}\),\(Y_{v\_train}\)) and a test set (\(X_{v\_test}\),\(Y_{v\_test}\)), and the training set (\(X_{v\_train}\),\(Y_{v\_train}\)) of simulation data is used to train the random forest classifier 1. In the test set (\(X_{v\_test}\),\(Y_{v\_test}\)) results, the hierarchical clustering algorithm is used to pick  
Figure 4: Digital Twin Assisted Transfer learning framework.  
the base classifier with the best performance in each cluster of the trained classifier 1 to form a new IRF1. The parameters in IRF1 are used to initialize the new classifier IRF2, and then the real data \((X_{p},Y_{p})\) in the training set are used to fine-tune IRF2. Finally, IRF2 is tested with the test set \((X_{P_{-}},Y_{P_{-}})\), and the test results are obtained.  
When everything described above is ready, we input the data generated by the physical entity into the algorithm for real-time fault diagnosis and analysis. Through the fault diagnosis results, the location of the fault can be quickly and accurately determined, and maintenance can be carried out through corresponding solutions.  
```
1:Input:
2:Train dataset:
3: (1) Large number of simulation data \((X_{p},Y_{p})\)
4: (2) A small amount of physical monitoring data \((X_{p},Y_{P})\)
5:Testing dataset: Physical monitoring dataset \((X_{P_{-}},Y_{P_{-}})\)
6:Training Period:
7:Step 1: Split the dataset \((X_{p},Y_{p})\) into \((X_{p_{-}},Y_{p_{-}})\) and \((X_{p_{-}},Y_{p_{-}})\)
8:Step 2: Train the RF classifier 1 with the dataset \((X_{p_{-}},Y_{p_{-}})\)
9:Step 3: Use a hierarchical clustering algorithm to select a new and IRF classifier 1 composed of the trees with the highest accuracy in each cluster within the dataset \((X_{p_{-}},Y_{p_{-}},test)\)
10:Step 4: Use the parameter of IRF1 as the initial parameter of the IRF classifier 2
11:Step 5: Train the IRF classifier 2 with a small amount of physical monitoring data \((X_{P},Y_{P})\)
12:Testing Period:
13:Step 6: Test the physical monitoring dataset \(X_{P_{-}}\)
14:Output: Physical monitoring data prediction label \(Y_{P_{-}}\)
```  
**Algorithm 1** Pseudocode of the Proposed Algorithm.