A hybrid artificial intelligence algorithm for fault diagnosis of hot rolled strip crown imbalance

Abstract

In the production process of hot continuous rolling, due to the imbalance between the number of normal cases and fault cases, the traditional supervised learning methods are often unable to deal with them efficiently. In order to address this problem, a new Hybrid Artificial Intelligence Algorithm is proposed, which is based on Light Gradient Boosting Machine (LightGBM) and combines resampling technique with Support Vector Machine (SVM) to deal with multi-class imbalance problems. Specifically, Support Vector Machines-Synthetic Minority Oversampling Technique (SVM-SMOTE) is used to increase the training data, so as to change the distribution of training data and improve the accuracy of the model, and the Particle Swarm Optimization (PSO) algorithm is used to find the hyper parameters of the model to determine the optimal hyper parameters combination. At the same time, the method of SHapley Additive exPlanation (SHAP) based on game theory is used to reveal the factors affecting the convexity of strip steel plate in hot rolling process and its interpretability. In check to see the validity of the model, experiments were carried out on UCI data set and hot rolling production data set, and multi-model comparison was carried out. The values of Geometric mean (G-mean), macroscopic F1 fraction (F1-Macro) and Matthews correlation coefficient (MCC) tested on the hot rolling data set are 0.992, 0.992 and 0.987 respectively. The model has a good effect. The results show that the model is significantly better than traditional models in solving the problem of strip convexity diagnosis during hot rolling process.