A Work-Centered Approach for Cyber-Physical-Social System Design: Applications in Aerospace Industrial Inspection

Industrial inspection automation in aerospace presents numerous challenges due to the dynamic, information-rich and regulated aspects of the domain. To diagnose the condition of an aircraft component, expert inspectors rely on a significant amount of procedural and tacit knowledge (know-how). As systems capabilities do not match high-level human cognitive functions, the role of humans in future automated work systems will remain important. A Cyber-Physical-Social System (CPSS) is a suitable solution that envisions humans and agents in a joint activity to enhance cognitive/computational capabilities and produce better outcomes. This paper investigates how a work-centered approach can support and guide the engineering process of a CPSS with an industrial use case. We present a robust methodology that combines fieldwork inquiries and model-based engineering to elicit and formalize rich mental models into exploitable design patterns. Our results exhibit how inspectors process and apply knowledge to diagnose the component’s condition, how they deal with the institution’s rules and operational constraints (norms, safety policies, standard operating procedures). We suggest how these patterns can be incorporated in software modules or can conceptualize Human-Agent Teaming requirements. We argue that this framework can corroborate the right fit between a system’s technical and ecological validity (system fit with operating context) that enhances data reliability, productivity-related factors and system acceptance by end-users.

[1]  Lene Pettersen,et al.  Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work , 2018, Work, Employment and Society.

[2]  Chris Baber,et al.  A Systematic Approach for Developing Decision Aids: From Cognitive Work Analysis to Prototype Design and Development , 2016, Syst. Eng..

[3]  Colin G. Drury,et al.  HUMAN FACTORS AND ERGONOMICS AUDITS , 2012, Handbook of Human Factors and Ergonomics.

[4]  Anand K. Gramopadhye,et al.  A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection , 2019, Automation in Construction.

[5]  Amy R. Pritchett,et al.  Computational Assessment of Authority and Responsibility in Air Traffic Concepts of Operation , 2016 .

[6]  Lena Osterhagen,et al.  Evaluation Of Human Work , 2016 .

[7]  C. G. Drury Human Reliability in Civil Aircraft Inspection , 2001 .

[8]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .

[9]  T L Johnson,et al.  How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection. , 2019, Applied ergonomics.

[10]  Ian Sommerville,et al.  Socio-technical systems: From design methods to systems engineering , 2011, Interact. Comput..

[11]  Jean-Christophe Le Coze,et al.  Outlines of a sensitising model for industrial safety assessment , 2013 .

[12]  Robert R. Hoffman,et al.  Influencing versus Informing Design, Part 1: A Gap Analysis , 2008, IEEE Intelligent Systems.

[13]  Zion Tsz Ho Tse,et al.  State-of-the-art technologies for UAV inspections , 2018 .

[14]  Eva Hornecker,et al.  The Elicitation Interview Technique: Capturing People's Experiences of Data Representations , 2016, IEEE Transactions on Visualization and Computer Graphics.

[15]  Peter Johnston The Aero-Engine Business Model: Rolls-Royce’s Perspective , 2017 .

[16]  Vincent G. Duffy,et al.  Towards augmenting cyber-physical-human collaborative cognition for human-automation interaction in complex manufacturing and operational environments , 2020, Int. J. Prod. Res..

[17]  Hussein A. Abbass,et al.  Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust , 2019, Cognitive Computation.

[18]  Karen M. Feigh,et al.  Shifting role for human factors in an ‘unmanned’ era , 2018 .

[19]  Dominik Dellermann,et al.  The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems , 2019, HICSS.

[20]  Francisco Chinesta,et al.  Intelligent assistant system as a context-aware decision-making support for the workers of the future , 2020, Comput. Ind. Eng..

[21]  Sondoss Elsawah,et al.  A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models. , 2015, Journal of environmental management.

[22]  Jeffrey M. Bradshaw,et al.  Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity , 2004, IEEE Intell. Syst..

[23]  Michael Diaz,et al.  Signal detection with criterion noise: applications to recognition memory. , 2009, Psychological review.

[24]  Fenghua Zhu,et al.  Cyber-physical-social system in intelligent transportation , 2015, IEEE/CAA Journal of Automatica Sinica.

[25]  Kathryn M. Kellogg,et al.  Decisionmaking in practice: The dynamics of muddling through. , 2017, Applied ergonomics.

[26]  Nuno Videira,et al.  Integrating Qualitative and Quantitative Methods in Participatory Modeling to Elicit Behavioral Drivers in Environmental Dilemmas: the Case of Air Pollution in Talca, Chile , 2018, Environmental Management.

[27]  Colin G. Drury,et al.  The Role of Visual Inspection in the 21st Century , 2017 .

[28]  Robert R. Hoffman,et al.  Challenges and Prospects for the Paradigm of Naturalistic Decision Making , 2017 .

[29]  Thomas Bruckner,et al.  A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda , 2019 .

[30]  Maxine Mackintosh,et al.  Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency , 2020, npj Digital Medicine.

[31]  David Maxwell Chickering,et al.  Machine Teaching: A New Paradigm for Building Machine Learning Systems , 2017, ArXiv.

[32]  C. D. Güss,et al.  What Is Going Through Your Mind? Thinking Aloud as a Method in Cross-Cultural Psychology , 2018, Front. Psychol..

[33]  Judi E. See,et al.  Visual inspection : a review of the literature. , 2012 .

[34]  Gary Klein,et al.  A naturalistic decision making perspective on studying intuitive decision making , 2015 .

[35]  José Dinis Silvestre,et al.  State-of-the-Art Review of Building Inspection Systems , 2016 .

[36]  F. Cabitza,et al.  The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence. , 2019, Annals of translational medicine.

[37]  Patrick Chisan Hew,et al.  Detecting Occurrences of the “Substitution Myth”: A Systems Engineering Template for Modeling the Supervision of Automation , 2017 .

[38]  Agnieszka Kujawińska,et al.  Human factors in visual quality control , 2015 .

[39]  Cynthia Breazeal,et al.  Machine behaviour , 2019, Nature.

[40]  Till Becker,et al.  Concept and Evaluation of a Method for the Integration of Human Factors into Human-Oriented Work Design in Cyber-Physical Production Systems , 2019, Sustainability.

[41]  N. Agrawal,et al.  Winning in the aftermarket , 2006 .

[42]  Don Norman,et al.  The challenges of automation in the automobile , 2019, Ergonomics.

[43]  Xiaochun Jiang,et al.  Theoretical issues in the design of visual inspection systems , 2004 .

[44]  Guy André Boy,et al.  Human–Systems Integration , 2020 .

[45]  Emilie M. Roth,et al.  Function Allocation Considerations in the Era of Human Autonomy Teaming , 2019, Journal of Cognitive Engineering and Decision Making.

[46]  William C. Adams,et al.  Conducting Semi‐Structured Interviews , 2015 .

[47]  Amadou Ndiaye,et al.  Formulating preliminary design optimization problems using expert knowledge: Application to wood-based insulating materials , 2018, Expert Syst. Appl..

[48]  P. Waterson,et al.  Recurring themes in the legacy of Jens Rasmussen. , 2017, Applied ergonomics.

[49]  Nicholas Ross Milton,et al.  Knowledge Acquisition in Practice: A Step-by-step Guide , 2007 .

[50]  Pierre Vermersch,et al.  L'entretien d'explicitation , 2014 .

[51]  Alain Bernard,et al.  Activity theory based context model: application for enterprise intelligent assistant systems , 2015 .

[52]  Azad M. Madni,et al.  Model‐based systems engineering: Motivation, current status, and research opportunities , 2018, Syst. Eng..

[53]  Maurice Pillet,et al.  The visual inspection of product surfaces , 2013 .

[54]  Bruce Edmonds,et al.  Different Modelling Purposes , 2019, Simulating Social Complexity.

[55]  Thierry Morineau,et al.  The heuristic version of Cognitive Work Analysis: A first application to medical emergency situations. , 2019, Applied ergonomics.

[56]  Nanning Zheng,et al.  Hybrid-augmented intelligence: collaboration and cognition , 2017, Frontiers of Information Technology & Electronic Engineering.

[57]  Veronique Limère,et al.  A structured methodology for the design of a human-robot collaborative assembly workplace , 2019, The International Journal of Advanced Manufacturing Technology.

[58]  B. Brehmer Dynamic decision making: human control of complex systems. , 1992, Acta psychologica.

[59]  Jacques Theureau,et al.  Les entretiens d'autoconfrontation et de remise en situation par les traces matérielles et le programme de recherche « cours d'action » , 2010 .

[60]  Lars-Ola Bligård,et al.  Visualising safety: The potential for using sociotechnical systems models in prospective safety assessment and design , 2019, Safety Science.

[61]  Guy A. Boy,et al.  Human-centered design of complex systems: An experience-based approach , 2017, Design Science.

[62]  Guy Andr Boy Tangible Interactive Systems: Grasping the Real World with Computers , 2016 .