A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics

TL;DR

This paper introduces a general data renewal model that adaptively updates industrial prediction algorithms over time, improving their accuracy amid changing machine conditions.

Contribution

It proposes a novel data renewal framework combining similarity and loss functions to dynamically update prediction models in industrial data analytics.

Findings

Effective identification of data changes

Improved prediction accuracy

Adaptive model updating demonstrated

Abstract

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.