A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics
This addresses the need for timely predictions in industrial processes like fault and yield prediction, but it is incremental as it builds on existing prediction algorithms.
The paper tackles the problem of prediction errors increasing over time in industrial data analytics due to changing machine conditions, proposing a general data renewal model that updates prediction models adaptively, and experiments show it improves prediction accuracy.
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.