## 4 Welding Multi-Source Sensing Systems and Their Information Processing and Knowledge Modeling
### Welding Multi-Source Information Knowledge Modelling and Model Test Validation  
To provide a more comprehensive and accurate description of the welding state, scholars have transitioned from relying on a single sensor to using multiple sensors. To maximize the use of data obtained from multiple sensors, they adopted multi-sensor information fusion techniques. Feature-level fusion is particularly effective due to its capability for significant information compression and its advantage in maximizing the information essential for decision analysis. This fusion first extracts features from each sensor data and then integrates these features to improve system performance, stability, and better understanding of the data. Therefore, in this paper, we propose to use feature level fusion to achieve the task of weld defect identification by obtaining 139 features of the molten pool image, 48 features of the vibration signal, and 8 features of the actual current value of the weld for six quality types of welds, namely, porosity, incomplete penetration, burnt-through, under-welded, good, and weld deviation. After the fusion process and utilizing the neighborhood rough set for feature fusion and attribute reduction, the resulting new decision system is used as an input for machine learning, and machine learning is used to achieve welding defect recognition. The multi-source information knowledge modeling process used in this paper is shown in Figure 11.  
Using the same base material, welding consumables, equipment, and operating environment as the ship's hull plate, we carried out welding process tests on simulated parts and obtained the collated test data to create a dataset. A total of 1920 sets of data were obtained. Among them, 335 groups are of porosity, 340 are of non-weld-through, 366 are of burn-through, 359 are of non-full weld, 358 are of good quality, and 162 are of weld deviation. Each of the three vibration signal directions (X, Y, and Z) has 16 features. Additionally, there are 8 current features and 139 visual image features, making a total of 195 features. Before diagnosing and identifying the defects, the experimental data are first preprocessed in the following three ways. * To ensure that large-valued features do not overshadow the importance of small-valued features during the classification process, we normalized each feature using Equation (18). \[x_{ij}^{\prime}=\frac{x_{ij}-x_{\text{jmin}}}{x_{\text{jmax}}-x_{\text{jmin}}}\] (18) where \(x_{ij}\) represents the sample of the \(j\) column of the \(i\) row, \(x_{\text{jmin}}\) represents the minimum value of the \(j\) column, and \(x_{\text{jmax}}\) represents the maximum value of the \(j\) column. * Outlier detection is then used to remove the outlier samples in the
* Due to the vast amount of data, direct training would be resource-intensive. Thus, attribute reduction is applied to this data table. The dataset is divided into an 80% training set and a 20% test set. The feature selection algorithm presented in this paper is employed to generate feature subsets, and its effectiveness and the advantages of the DOA-SVM model are verified. After the parameter optimization of DOA on SVM, the highest classification accuracy is achieved at \(c=1024\), \(g=9.76*10^{-5}\). The neighborhood radius \(\delta=0.2\) and the kernel function RBF are set to train and test the support vector machine. Ten trials were conducted, and the average test  
Figure 11: Multi-source information knowledge modeling process. classification accuracy from these trials was used to evaluate each classification algorithm, including CART, SVM, and DOA-SVM. The trials were grouped into three sets:  
* The sampled dataset undergoes no processing and is directly used for training and testing. * The classical rough set theory's attribute reduction algorithm is applied to approximate the attributes of the sampled dataset, after which the approximated set is used for training and testing.