  In industrial data analytics, one of the fundamental problems is to utilize
the temporal correlation of the industrial data to make timely predictions in
the production process, such as fault prediction and yield prediction. However,
the traditional prediction models are fixed while the conditions of the
machines change over time, thus making the errors of predictions increase with
the lapse of time. In this paper, we propose a general data renewal model to
deal with it. Combined with the similarity function and the loss function, it
estimates the time of updating the existing prediction model, then updates it
according to the evaluation function iteratively and adaptively. We have
applied the data renewal model to two prediction algorithms. The experiments
demonstrate that the data renewal model can effectively identify the changes of
data, update and optimize the prediction model so as to improve the accuracy of
prediction.
