## 1 Introduction
(3) For the binary classification and diagnosis of data in the fault field, conventional algorithms tend to cause the convergence speed of the data aggregation classification process to be too fast, resulting in the sudden boundary fitting ignoring the knowledge content of the boundary, which is not conducive to the observation of some boundary knowledge [7]. According to the data in the field of equipment safety diagnosis, it is very important to reduce the convergence rate of sampling for the construction and use of knowledge graphs. (4) None of the above algorithms deal with noise reasonably. In fact, the processing of noise directly affects the distribution of new instances, that is, directly affects the sampling effect of the algorithm. In the field of conventional production line fault detection, the main form of data is the binary classification problem, that is, fault state and normal state. The subsequent fault cause analysis is a data processing problem that requires multi-data and multi-dimensional comprehensive analysis, but the binary classification problem of the first step is the primary task to achieve early warning and production line safety assurance [8]. Through the analysis of operation and maintenance data, there are serious class imbalance and noise problems in the production line fault detection data. In order to solve the above problems, this paper proposes a majority-class weighted minority-class oversampling technique based on fuzzy rough instances combined with classification fusion iterative filtering. The algorithm can effectively solve the problem of sample imbalance in binary classification problems and can significantly reduce the overfitting failure of synthetic samples to the original clustering problem. Subsequently, the classifier can integrate iterative filtering to filter out the influence of noise as a whole and finally complete the classification task. The algorithm shows good ability in solving practical problems. This study first introduces the basic principle of the algorithm and then adopts 8 public data sets and 5 similar algorithms of other scholars as the experimental basis and verifies the effectiveness of the algorithm by using the measurement standards commonly used in the industry (accuracy, G-mean, and F-measure). (Note: In order to ensure the accuracy of the experiment, the randomness of the experiment was ignored in this study, and the 5-fold stratified crossover experiment was adopted and repeated three times.) Based on the above experimental design and results, the proposed majority-class weighted minor-class oversampling technique is discussed and evaluated in depth. First of all, from the experimental results, this algorithm shows a higher classification accuracy and a lower false positive rate when dealing with the binary classification problem of fault detection in the production line. This is mainly due to the algorithm's effective handling of the problem of sample imbalance and the filtering ability of noisy data. By comparing similar algorithms of other scholars, the algorithm proposed in this paper has improved in various performance indicators, especially in the two comprehensive evaluation indicators of G-mean and F-measure, showing better performance. Second, the algorithm design of this paper fully considers the actual demand of production line fault detection. In the production line environment, the fault data are often relatively rare, and the normal data occupies the vast majority. This unevenness of data distribution can have a serious impact on the performance of the classifier. The algorithm proposed in this paper alleviates this problem effectively by means of weighting and oversampling so that the classifier can better adapt to the actual data distribution. In addition, the iterative filtering mechanism in the algorithm also greatly improves the robustness and stability of the classifier. In the production line environment, noisy data are difficult to avoid. These noisy data may negatively affect the performance of the classifier.