A Hybrid Degradation Prediction Model for the Flexible Shaft Pulling Device in Crystal Growth Furnace

Abstract

Flexible shaft pulling device is a key motion device in the process of single crystal silicon preparation. The health status of the pulling device has direct influence on crystal quality. In this paper, a hybrid model is proposed for degradation prediction of the pulling device when the monitoring data is limited and with unknown statistical distribution of degradation data. Firstly, a modified filter-based degradation prediction method is designed, where a new resampling strategy is proposed to alleviate the particle degeneracy. Then, a neural network is designed to correct the potential uncertainties due to the change of the load of the pulling device. The prediction error of the filter-based method is further used to adjust the parameters of the network. Finally, experiments are conducted to verify the performance of the proposed method using both the standard dataset and data obtained from the flexible shaft pulling device.