A General Methodology for Adapting Industrial HMIs to Human Operators

Villani, Valeria; Loch, Frieder; Vogel‐Heuser, Birgit; Fantuzzi, Cesare

doi:10.1109/tase.2019.2941541

Cited by 19 publications

(20 citation statements)

References 24 publications

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“…This means that an element on a display may belong to more workflows, and a workflow may be represented on more displays. The number of elements in a given display cannot exceed a threshold C due to the cognitive limits of the operators with respect to signal perception [23]. The goal is to minimise the number of displays allocated to a workflow to ensure that the operator does not have to switch too much between different displays when solving a frequently occurring problem.…”

Section: Connection Between Workflows and Displaysmentioning

confidence: 99%

Machine Learning-Supported Designing of Human–Machine Interfaces

Bántay,

Abonyi

2024

Applied Sciences

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The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.

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Section: Connection Between Workflows and Displaysmentioning

confidence: 99%

Machine Learning-Supported Designing of Human–Machine Interfaces

Bántay,

Abonyi

2024

Applied Sciences

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“…Mindful developers operate according to the principle "the customer is always right" [36], understanding that what matters is how the user, not the producer, perceives the product. The system designer should adapt their solutions to the level of knowledge, experience, cultural conditions, technical and technological capabilities, and ultimately the mindset of the final user [37]. Well-conducted product usability tests on representatives of the target group allow the creator to verify the design assumptions [38].…”

Section: Usability Testing-backgroundmentioning

confidence: 99%

Extended Reality in Education and Training: Case Studies in Management Education

et al. 2022

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This paper presents and concludes the ATOMIC project, which was to create an XR-based educational environment that enables students to meet the challenges of a natural business environment such as planning and organizing, staffing and control, problem solving, critical thinking, creativity, and teamwork. Four different approaches were taken utilizing different XR technologies (projector-based AR, mobile-based AR, HMD AR, and HMD VR), and their efficacy and educational value were juxtaposed. A universal 34-question usability questionnaire was proposed that can be applied in future XR usability studies. Four versions of the application were tested among 20 students to identify the advantages and disadvantages of each approach in an educational context.

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“…It is possible to implement immersive training environments to train employees [2], [3], [4], [5]. Such training can be supplemented by human-machine interfaces (HMIs) that adapt to operators' skills and capabilities [1], [6], [7], [8], [9]. However, designing and implementing new education programs, immersive workplace training systems, and adaptive HMIs require enormous effort and investment.…”

Section: Introductionmentioning

confidence: 99%

Digitalization and the Future of Employment: A Case Study on the Canadian Offshore Oil and Gas Drilling Occupations

Wanasinghe

Gosine

Petersen

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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This paper presents a novel approach to identifying reskilling requirements, job merging pathways, and a tentative timeline for transforming offshore oil and gas drilling occupations amid the fourth industrial revolution (industry 4.0). The proposed algorithm focuses on potential job merging due to technological adoption. It introduces a scaling factor named digital readiness level to incorporate modulation factors (e.g., cost of development and deployment of new technologies, labour market dynamics, economic benefits, regulatory readiness, and social acceptance) that act as catalysts or hindrances for technology adoption. A feature-based approach is developed to assess the similarities between occupations, while a mathematical model is developed to project automation trajectories for each job under investigation. These facilitate the consideration of potential job merging scenarios and the associated timeline. Since technology adoption depends on the industry, region, occupation, and stakeholder's ability to manage the transformation, the proposed algorithm is presented as a case study on Canadian offshore oil and gas drilling occupations. However, this algorithm and approach can be applied to other industries or occupation structures. The proposed algorithm projects that the total number of personnel on board (POB) in a typical offshore drilling platform will be reduced to six by 2058. A sensitivity analysis was conducted to assess the robustness of the proposed algorithm against variations in the feature values and weighting factors. It was found that when changing feature values and weighting factors up to ±20% of their original values, only one job that remains after 2058 follows three different job merging pathways, while others remain unchanged. Even the job that followed three different pathways was composed of the same source jobs compared to the corresponding job in the baseline results.Note to Practitioners-This research is inspired by the ongoing digital transformation initiatives and their socioeconomic impact.

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A General Methodology for Adapting Industrial HMIs to Human Operators

Cited by 19 publications

References 24 publications

Machine Learning-Supported Designing of Human–Machine Interfaces

Machine Learning-Supported Designing of Human–Machine Interfaces

Extended Reality in Education and Training: Case Studies in Management Education

Digitalization and the Future of Employment: A Case Study on the Canadian Offshore Oil and Gas Drilling Occupations

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