Incipient fault diagnosis method for silicon single crystal growth assisted by deep learning

Abstract

In this study, an accurate diagnostic classification algorithm based on a deep belief network and entropy value (C-DBN-E) incorporating signal decomposition, entropy theory and deep belief network (DBN) network is proposed to address the problem of tiny faults that may occur during the growth of silicon single crystals (SSC). The study is divided into three stages: feature selection, fault feature extraction and fault pattern classification. In the feature selection stage, the recursive feature elimination (RFE) method based on support vector machine (SVM) is employed to rank features according to their importance and eliminate redundant features. In the subsequent feature extraction stage, the adaptive decomposition theory is introduced to select components that contain a greater number of fault features for signal reconstruction. The deep features of the reconstructed signal are then mined in depth using a DBN and supplemented with energy entropy and multi-scale arrangement entropy as supplementary features to further refine the capture of tiny fault information. Finally, all the features are integrated and inputted into the SVM classifier to enable accurate classification of micro-fault modes in SSC growth. The effectiveness of the method in distinguishing micro-faults in the SSC growth process is verified by simulation results. This study is of great significance in solving the problem of fault diagnosis in the SSC growth system, which is expected to contribute to the improvement of work performance and the reduction of accident risk and economic loss.