Supporting data for Detection of Detrimental Weak Emergent Behavior Considering Operational Factors: a Case Study in Search and Rescue

DATASET MIGRATED FROM FIGSHARE: This dataset contains the input and output data from an industrial case study aiming to detect undesired non-intuitive behavior for an engineered system in an operational context (an Autonomous Surface Vessel (ASV) on a Search and Rescue (SAR) mission). We used the Taguchi method to set up experiments, conducted the experiments in a case company specific test arena, and performed multiple linear regression (MLR) analysis.The speadsheet consists of 10 sheets:Additional theory for DoE and regressionScreening experimentsTransition rationale from screening to investigationInvestigation experiments for system setting 1Investigation experiments for system setting 2Plots for system setting 1Plots for system setting 2Fractional Factorial vs Taguchi studyAdditional trial and error experimentsDimensional analysis experimentsArticle Abstract - Related PublicationThis paper applies the Design of Experiments approach for detecting detrimental weak emergent behavior of an Autonomous Surface Vessel operating in a dynamic environment on a Search and Rescue mission. The research utilizes Orthogonal Arrays in combination with regression analysis to systematically test the parameter space of an engineered system function. We used Orthogonal Arrays first to detect, and later in analyzing, the parameter space where the system model does not comply with a defined Measure of Effectiveness. The findings from this case study suggest that these methods enable a systematic exploration of the system’s parameter space, allowing for effective detection of detrimental weak emergent behavior. This approach potentially enhances test coverage, expands system operating knowledge, and facilitates mitigation efforts more efficiently.