Risk identification and assessment of human-machine conflict

Wen, He (2023) Risk identification and assessment of human-machine conflict. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Abstract

The process industries are fully embracing digitalization and artificial intelligence (AI). Industry 4.0 has also transformed the production structures in the process industries to increase productivity and profitability; however, this has also led to emerging risks. The rapid growth and transformation have created gaps and challenges in various aspects, for example, information technology (IT) vs. operation technology (OT), human vs. AI, and traditional statistical analysis vs. machine learning. A notable issue is the apparent differences in decision-making between humans and machines, primarily when they work together. Contradictory observations, states, goals, and actions may lead to conflict between these two decision-makers. Such conflicts have triggered numerous catastrophes in recent years. Moreover, conflicts may become even more elusive and confusing under external forces, e.g., cyberattacks. Therefore, this thesis focuses on human-machine conflict. Five research tasks are conducted to explore the risk of human-machine conflict. More specifically, the thesis presents a systematic literature review on the impact of digitalization on process safety, highlights the myths and misconceptions of data modeling on process safety analysis, and attempts to clarify associated concepts in the area of human-machine conflict. In addition, the thesis summarizes the causes of conflicts and generalizes the mathematical expressions of the causes. It illustrates the evolutional process of conflicts, proposes the measurement of conflicts, develops the risk assessment model of conflicts, and explores the condition of conflict convergence, divergence, and resolution. The thesis also iii demonstrates the proposed methodology and risk models in process systems, for example, the two-phase separator and the Continuous Stirred Tank Reactor (CSTR). It verifies the conflict between manual and automated control (e.g., proportional-integral-derivative control (PID) and model predictive control (MPC)). This thesis proves that conflict is another more profound and implicit phenomenon that raises risks more rapidly and severely. Conflicts are highly associated with faults and failures. Various factors can trigger human-machine conflict, including sensor faults, cyberattacks, human errors, and sabotage. This thesis attempts to provide the readers with a clear picture of the human-machine conflict, alerts the industry and academia about the risk of human-machine conflict, and emphasizes human-centered design.

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