Evaluating Control Room Operator Training Outcomes Through Eye Gaze Augmented Multi-Scale Data
01 Jan 2021-Vol. 50, pp 1307-1312
TL;DR: This work uses 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, to gauge the development of operator’s mental models during training to understand the transition from novice to becoming experts.
Abstract: 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.
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TL;DR: In this article, 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 is proposed to quantify the progress of a novice operator's learning.
7 citations
TL;DR: In this paper , the authors review the literature on the applications of VR to chemical safety in laboratories and industries and stress the need to incorporate physiological sensors into the VR environment, and discuss the best practices for VR-based training.
2 citations
TL;DR: In this article , the cognitive behaviors displayed by expert operators can be represented as target values on the HMM's state transitions and emission probability distributions, which 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.
Abstract: 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. • Operator training is critical to ensure safe operations. • Traditional training programs assess learning using expert judgment with its inherent variability and biases. • Using the HMM model developed in Part 1, we specify target values of HMM parameters that indicate expertise. • We propose two axioms of learning that can capture the evolution of the operator's mental models during training. • Human subject studies confirm that the proposed model accurately reflects the progress of learning.
1 citations
01 Jan 2022
TL;DR: In this paper , the role of human factors in understanding the interaction between humans and other elements of the work system is discussed and a detailed discussion on how human failures can be quantified by using human reliability assessment techniques.
Abstract: With advancements in technology and sophistication, the role of humans in the process industries has transformed from predominantly manual operations to one primarily involving monitoring, diagnosis, and prognosis. These tasks are cognitively challenging as they involve the acquisition and processing of large amounts of information. Human errors during such operations can be catastrophic. In this chapter, we discuss the role of human factors in understanding the interaction between humans and other elements of the work system. We also discuss a framework to systematically account for human errors. Subsequently, we provide a detailed discussion on how human failures can be quantified by using human reliability assessment techniques. Finally, given the changing nature of the roles of individuals, owing to digitalization, we discuss the role of physiological measurements in guiding the application of human factors principles for evaluating and enhancing human performance.
1 citations
TL;DR: In this article , a human digital twin (HDT) was developed to simulate a control room operator's behavior, even during various abnormal situations, using the ACT-R cognitive architecture.
Abstract: To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators’ performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators’ cognitive behavior. In addition, these cannot be used to predict operators’ expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator’s behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT’s ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT’s cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator’s mental models. Additionally, the HDT can also enhance operators’ decision-making during real-time operation.
References
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TL;DR: The operator training simulator (OTS) as discussed by the authors is an alternative alternative to train operators without actually endangering the plant and personnel, and it has been successfully used in many chemical industries.
Abstract: Abstract An inevitable need for the skilled operators to increase the safety and productivity is not new to the chemical industry. Consequently, the training of operators is considered as a very important activity in the chemical industry. Conventional training methodologies are ineffective in training the operators for seldom-occurring perilous situations. The operator training simulator (OTS) is an alternative to train operators without actually endangering the plant and personnel. This contribution covers and discusses the need for OTS, applications of OTS in the chemical industry, issues related to OTS, salient features of a good OTS, commercial software packages used to build OTS, and training configurations. In this article, applications of OTS in the chemical industry reported in the open literature from 1990 to mid-2013 are also reviewed briefly. The review shows that OTS has been successfully used in many chemical industries. Finally, this article concludes by outlining the future directions. Overall, it provides a deeper understanding of many issues about the OTS to interested researchers, vendors, modeling engineers, and application engineers aiming to stimulate further developments in this area leading to improved OTS and their increased usage in the chemical industry.
51 citations
13 May 2014
TL;DR: It is concluded that advancements in the applications of process control techniques call for a new mindset in the training of operators, and traditional training practice could be advanced by using current training environments, such as virtual reality training simulators.
Abstract: OCCUPATIONAL APPLICATIONS Operators play a vital role in production and safety in industrial processes. Since the introduction of advanced control techniques, such as model predictive control and real-time optimization, operators’ acquisition of adequate mental models to develop complex cause-and-effect relationship explaining plant behavior has been increasingly challenged. Additionally, distinct challenges have arisen with respect to crew coordination between control room and field operators to orchestrate a coordinated flow of actions to assess situations or choose a course of action. Based on an analysis of training needs, it is argued that traditional training practice, such as the use of operator training simulators, could be advanced by using current training environments, such as virtual reality training simulators. This would allow using modern training technology and its advancements in parallel to the advancements of control techniques to support production and safety at its best.TECHNICAL ABST...
42 citations
TL;DR: This paper develops this cognitive engineering based approach and proposes novel quantitative measures of operators’ situation awareness based on eye gaze dynamics and demonstrates that the proposed measures reliably identify the situation awareness of the participants during various phases of abnormal situation management.
39 citations
TL;DR: Research indicates that application of process synthesis methodologies for simultaneous inherent safety assessment and advanced cognitive engineering approaches for human error reduction will lead to enhanced process safety.
33 citations
02 Oct 1994
TL;DR: Call for a paradigmatic shift in the approach to training through the design of the work environment as an enhanced learning environment to improve the operability and safety of operations under abnormal situations.
Abstract: A persistent paradox in the domain of supervisory control is that as automation technology advances in complexity and sophistication, operations professionals are faced with increasingly complex decisions in managing abnormal situations. An abnormal situation management (ASM) solution concept team was established to identify current limitations facing industrial plant operations during abnormal conditions. We observed and interviewed a broad range of personnel at six plants in the US and Europe. Several critical areas were identified as important to the improvement of the operability and safety of operations under abnormal situations. In this paper, we focus on the specific area of training personnel to manage abnormal situations. We call for a paradigmatic shift in the approach to training through the design of the work environment as an enhanced learning environment. >
29 citations