Babji Srinivasan

Process plant operators rely on their knowledge of process cause-and-effect relationships during abnormal situation management. Novice operators develop such process knowledge during training. Hence, holistic assessment of operators’ training is essential to ensure process safety. Currently, during training, operators’ process understanding is evaluated using criteria such as successful completion, task based measures, and operator actions that ignore their cognitive behavior. In this work, we propose an eye-tracking-based approach that uses the operator's attention allocation during different pre-specified training scenarios along with process data, alarm information, and operator actions. Our approach is based on the precept that an operator would focus their attention on those variables on the human-machine interface that they believe have a direct causal relationship to the situation at hand. Also, expert operators seek time-based information for proactive monitoring. Accordingly, to quantify the progress of a novice operator's learning, we develop two metrics — association metric and salience metric — using correspondence analysis of operators' eye gaze. To evaluate the applicability of the metrics, we conducted experiments with ten participants who performed 486 tasks. Statistical studies reveal that the proposed metrics can quantify operators’ learning and thus can be used to objectively evaluate training effectiveness.

The significance of operator training has dramatically increased due to complex automation strategies in modern process plants. It is reported that human errors account for 70% of the accidents in process industries, with inadequate training cited as one of the most common reasons for these incidents. Our previous work has shown the potential of eye-tracking to infer the mental state of control room operators. In this work, we propose a methodology that combines multi-scale data from the process simulator, control actions performed, and eye gaze data of the operators to evaluate their training outcomes. Specifically, we use fixation transition entropy, an eye-tracking metric, which can help infer the mental models of the process abnormalities developed by the operators during repeated control room tasks. Results indicate that the fixation transition entropy decreases on account of development of correct mental models of process while it remain at higher values when operator fails to update their mental models during plant abnormalities. Thus, the proposed metric can be used to gauge the development of operator's mental models during training to understand the transition from novice to becoming experts.

Operator training is critical to ensure safe operation in safety-critical domains such as chemical process industries. Training enhances the operator's understanding of the process, which is then encapsulated as mental models. Typically, the operator's learning in traditional training programs is assessed using expert judgment or in terms of process- and operator action-based metrics. These assessment schemes, however, ignore the cognitive aspects of learning, such as mental model development and cognitive workload. The HMM-based model proposed in Part 1 offers a systematic way to quantify operators' cognition during abnormalities. In this Part 2, we show that the cognitive behaviors displayed by expert operators can be represented as target values on the HMM's state transitions and emission probability distributions. Further, we propose two axioms of learning that can capture the evolution of the operator's mental models as they learn the causal relationships in the process and gain expertise in handling abnormal situations. We validate the proposed axioms by conducting training experiments involving 10 participants performing 486 tasks. Our results reveal that the axioms can accurately assess the progress of operators' learning.

In a typical process industry, Human Machine Interfaces (HMIs) are essential for all aspects of communication and interaction between operators and processes, vital for process safety, quality, and efficiency. Good interface design enables operators to accomplish their duties efficiently and effectively with minimal errors. Consequently, it is crucial to design HMIs in a way that facilitates collaboration between automation and operators. Current HMI design practices have a lot of subjectivity and often ignore operator's cognitive behavior. In this work, we propose a quantitative approach that uses cognitive metrics – association and salience metrics – obtained using eye-tracking to improve HMI design for process operators. The association metric helps in identifying information sources which are often used together while salience metric informs about information sources which are extensively used from the HMI. Based on insights from these cognitive metrics, we designed a new HMI. To evaluate the relative efficacy of each HMI, human subject studies were conducted. The results indicate that the new HMI developed with the use of cognitive metrics improved the operators' efficiency. Thus, the proposed approach can help enhance usability of industrial HMIs.

Operator performance is key to ensuring safety in process industries. Therefore, a comprehensive assessment of their performance is critical for smooth and efficient plant operation. Traditional performance assessments are not comprehensive as these ignore cognitive aspects of performance. On the other hand, while eye-tracking-based approaches do provide a cognitive assessment during operating training, their applicability in a real-time real setting is limited. Existing eye-tracking systems come with many constraints affecting users' mobility (restricted movement in all directions) in their environment. In addition, it is beyond the scope of current eye trackers to track multiple users working in an environment, such as in control rooms. Satisfying the above requirements makes these eye-trackers expensive. In this work, we demonstrate the capabilities of an in-house developed cost-effective eye-tracking system to track users' eye movement in an unconstrained environment while giving freedom of head movement. Human subject studies are conducted to compare operators' gaze patterns and the quality of data with that obtained using commercial eye trackers. The results generally agree with data quality obtained using commercial eye trackers. Hence, the performance of the developed eye-tracking system is comparable to existing commercial eye-trackers while overcoming their limitations, such as restricted user movement, single-user tracking, and high cost.