Research on Operator Support Functions for Process Industries Using Deep Learning Technology

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K04113
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 18020:Manufacturing and production engineering-related
• Research Institution: Waseda University
• Principal Investigator: Fujimura Shigeru 早稲田大学, 理工学術院(情報生産システム研究科・センター), 教授 (00367179)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 異常診断 / 時系列予測 / プロセス監視 / 深層学習 / 機械学習

Outline of Final Research Achievements

In this research, we realized an operator support function that can be used in actual processes by applying deep learning technology to time-series data accumulated by a process control monitoring system. Specifically, we proposed a new deep learning model that predicts multiple sensor data of an actual chemical process. The model learns complex relationships among the sensor data being monitored for chemical process control. The model utilizes various time-length influence relationships between related sensor data to implement normal value prediction for a single sensor data.
The developed model has realized an operator support function that prevents errors by experienced operators and provides awareness of process knowledge to new operators. We proposed a method to construct a model using deep learning based on a huge amount of normal process time-series data, and realized a customization-less system construction method that automatically constructs the system.

Free Research Field

生産管理、システム工学

Academic Significance and Societal Importance of the Research Achievements

正常時の複数の時系列データを入力情報とし、時系列データの将来の挙動を予測するモデルを開発し、熟練オペレータに対するポカミス防止、新人オペレータに対するプロセス知識における気づきを与えるオペレータ支援機能を実現した。膨大な正常時のプロセス時系列データを利用してディープラーニングによってモデルを構築する方法を提案し自動的にシステムを構築するカスタマイズレスなシステム構築手法を実現した。