An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors

Huixin Tian,Minwei Shuai,Kun Li,Xiao Peng

doi:10.1155/2019/5353296

Abstract

With the continuous improvement of automation in industrial production, industrial process data tends to arrive continuously in many cases. The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms. According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data. The ILES aggregates multiple sublearning machines by different weights for better accuracy. When new data set arrives, a new submachine will be trained and aggregated into ensemble soft sensor model according to its weight. The other submachines' weights will be updated at the same time. Then a new updated soft sensor ensemble model can be obtained. The weight updating rules are designed by considering the prediction accuracy of submachines with new arrived data. So the update can fit the data change and obtain new information efficiently. The sizing percentage soft sensor model is established to learn the information from the production data in the sizing of industrial processes and to test the performance of ILES, where the ELM (Extreme Learning Machine) is selected as the sublearning machine. The comparison is done among new method, single ELM, AdaBoost.R ELM, and OS-ELM, and the test of the extensions is done with three test functions. The results of the experiments demonstrate that the soft sensor model based on the ILES has the best accuracy and ability of online updating.

Highlights

During industrial processes, plants are usually heavily instrumented with a large number of sensors for process monitoring and control
According to the characteristics of industrial production process, we address an incremental learning ensemble strategy (ILES) that incorporates incremental learning to extract information efficiently from constantly incoming data
We present a new incremental learning ensemble strategy with a better incremental learning ability to establish the soft sensor model, which can learn additional information from new data and preserve previously acquired knowledge

Summary

Introduction

Plants are usually heavily instrumented with a large number of sensors for process monitoring and control. In this paper, we mainly focus on how to add the incremental learning capability to the ensemble soft sensor modeling method and hopefully provide useful suggestions to enhance both the generation and online application abilities of soft sensors for industrial process. Aiming at the demands of soft sensors for industrial applications, a new detection strategy is proposed with multiple learning machines ensembles to improve the accuracy of the soft sensors based on intelligent algorithms. This kind of methods is not good enough because some good performances have to be lost to learn new information [21] Against this background, we present a new incremental learning ensemble strategy with a better incremental learning ability to establish the soft sensor model, which can learn additional information from new data and preserve previously acquired knowledge. We summarize our conclusions and highlight future research directions

The Incremental Learning Ensemble Strategy

Experiments

Conclusions

Method

Findings

Method ELM