A multi-stage fault prediction method of continuous casting machine based on Weibull distribution and deep learning

Abstract

When predicting the failure of large complete equipment such as continuous casting machines, it is usually difficult to obtain full life cycle failure data of core equipment such as continuous casting rollers. The strategy based solely on reliability distribution cannot guarantee the accuracy of fault prediction, while the strategy based solely on deep learning cannot predict medium and long-term fault trends. Aiming at the problem, a multi-stage fault prediction model TCN-BiGRU-WD for continuous casting rollers is built. The fault data of continuous casting roll is firstly input into TCN for feature extraction, and then the extracted features are input into BiGRU. Not only the short-term fault rate with high accuracy is output, but also the shape parameter(k) and scale parameter(λ) of Weibull distribution (WD) are output, so as to obtain the medium and long-term fault rate function (f(t;λ,k)) of continuous casting roll in the future. An example shows that the proposed multi-stage fault prediction model can not only improve the prediction accuracy of the short-term fault rate of continuous casting roll, but also expand the ability to predict the long-term fault trend in the future.