Abstract
In this paper, auto-regressive integrated moving average (ARIMA) time-series data forecast models are evaluated to ascertain their feasibility in predicting human–machine interface (HMI) state transitions, which are modeled as multivariate time-series patterns. Human–machine interface states generally include changes in their visually displayed information brought about due to both process parameter changes and user actions. This approach has wide applications in industrial controls, such as nuclear power plant control rooms and transportation industry, such as aircraft cockpits, etc., to develop non-intrusive real-time monitoring solutions for human operator situational awareness and potentially predicting human-in-the-loop error trend precursors.
Highlights
Operator situational awareness (SA) is quintessential in ensuring safe operation of any industrial establishment and more so in nuclear power plants (NPP)
As evident from the post-accident reports [2,3,4], include: (1) reduction in situational awareness owing to human factor related deficiencies in legacy human–machine interface (HMI design); (2) normalization to deviance to lax nuclear safety culture; (3) information overload owing to the rapid rate at which information was presented to operators via the control room HMIs; and (4) incorrect mental model of highly dynamic unit evolutions resulting in cognitive errors, owing to conflicting plant information supplied by failed or faulty sensors and incorrect field equipment status monitoring
The assumptions outlined in section (Section 3.1) allows HMI state TS data to promise being weakly stationary in nature, allowing application of auto-regressive integrated moving average (ARIMA) forecast models
Summary
Operator situational awareness (SA) is quintessential in ensuring safe operation of any industrial establishment and more so in nuclear power plants (NPP). As evident from the post-accident reports [2,3,4], include: (1) reduction in situational awareness owing to human factor related deficiencies in legacy human–machine interface (HMI design); (2) normalization to deviance to lax nuclear safety culture; (3) information overload (looking-but-not-seeing effects [5]) owing to the rapid rate at which information was presented to operators via the control room HMIs (panel indications, annunciations, etc.); and (4) incorrect mental model of highly dynamic unit evolutions resulting in cognitive errors, owing to conflicting plant information supplied by failed or faulty sensors and incorrect field equipment status monitoring. The rest of the paper is organized as follows: Section 2 presents a review of current state-of-the-art relevant to proposed time-series data forecasting and its applications.
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