The effects of automation-induced complacency on fault diagnosis and management performance in process control

Abstract

Operators of automated systems can develop complacency, impairing their ability to respond in a timely and appropriate fashion when automation fails. This study sought to examine the impact of an instructional manipulation of attentiveness, in terms of engagement and accountability, on fault diagnosis and fault management. Participants trained on the operation of a simulated process control task, with instructions varied to induce higher or lower attentiveness to the task. After several routine faults within the system, a fault occurred along with a failure of a previously available diagnostic and management aid, and shortly after a second failure occurred. The first failure was associated with significant impairment of diagnosis and management, but comparatively few differences between attentiveness groups. Results are discussed in terms of their implications for a model of human-automation interaction.

Complacency and Automation Bias in the Use of Imperfect Automation

“…To address the objective of evaluating FFs of decision aiding across automation failure types, in the current experiment in a between-subjects design, three groups of participants managed AutoCAMS 2.0, the microworld process control simulation employed by Bahner et al (2008), Manzey et al (2012), and Clegg, Vieane, Wickens, and Sebok (2014). Operators managed a series of trials in AutoCAMS during which they experienced a series of "routine system malfunctions" (e.g., stuck valves that could be diagnosed and fixed using procedures).…”

Using Empirical Research and Computational Modeling to Predict Operator Response to Unexpected Events

“…This effect was found to be well supported in a meta-analysis to investigate the effects of degree of automation on operator performance (Onnasch et al, 2013).  Cognitive complexity: we are currently performing targeted HITL experimentation to gather data to support and quantify this intuitively obvious effect (Clegg, Vieane, et al, 2014). From informal observations with the AutoCAMS environmental process control simulation, we have found that participants experience the greatest difficulty in diagnosing the most complex (i.e., mixer system) failures.…”

Fault Effect Analysis based on a modelling Approach for Requirements Functions and Components

“…In the active management, unlike known problems, the problem management system must identify the effect of defect components in accordance to the fault diagnosis results [3]. Then the available functions are determined by the problem management system.…”