Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

Fault prediction has arisen as a basic monitoring strategy that predicts an abnormal event occurring in near future based on the current symptoms observed in a process. Such a proactive approach helps in taking an appropriate action beforehand so as to mitigate the impact a fault can have on a process. Recently, data-driven modeling techniques have been widely used due to an increased accessibility to process data. Though the offline trained models are successful in modeling complex dynamics, they have limited ability in capturing the dynamic process behavior, especially under abnormal conditions. To address this issue, we utilize an adaptive modeling technique called operable adaptive sparse identification of systems (OASIS) that can cope with any dynamical changes. Based on the forecasted process behavior using OASIS, we perform risk-assessment to predict faults and assess risk. In the proposed method, risk is used as a criteria to monitor and manage process operation. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Fault prediction is a monitoring strategy that predicts abnormal events based on current symptoms, data-driven modeling techniques are widely used but offline models have limitations in capturing dynamic process behavior. An adaptive modeling technique called OASIS is proposed to address this issue for risk assessment and fault prediction.