Continual Semisupervised Learning of Echo State Network for Quality Prediction of Multimode Processes

Chao Yang, Qiang Liu*, Yi Liu, Yiu-ming Cheung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

The successive switching nature of multimode processes, coupled with data scarcity, challenges traditional quality prediction models. Specifically, the difficulty of simultaneously collecting abundant labeled datasets from all modes forces the model to update its parameters as modes switch. This leads to the forgetting of historical mode knowledge and hinders the aggregation of knowledge, thereby degrading generalization across modes. To this end, we propose a novel continual semisupervised graph echo state network (CS2GESN). First, a semisupervised graph echo state network (S2GESN) is designed based on the graph smoothing assumption to extract dynamic information from unlabeled samples within each mode. The S2GESN model then evolves into a continual model, CS2GESN, employing an elastic weight consolidation strategy for parameter importance estimation derived from pseudoinverse parameter optimization, facilitating the accumulation of historically learned knowledge. This manner alleviates performance deterioration from data scarcity and information forgetting, and enables more flexible modeling of successive arriving operating modes. The superiority and feasibility of the proposed method are demonstrated through its application to the Tennessee Eastman process and the three-phase flow facility process.

Original languageEnglish
Number of pages11
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusE-pub ahead of print - 12 Jun 2025

User-Defined Keywords

  • Catastrophic forgetting
  • continual learning (CL)
  • echo state network (ESN)
  • multimode processes
  • quality prediction
  • semisupervised learning (SSL)

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