An Accident Causation Analysis and Taxonomy (ACAT) model of complex industrial system from both system safety and control theory perspectives

Abstract Accident causation analysis is a good way to trace industrial accident causes and ultimately to prevent similar accidents from happening again. Classification of accident causes can not only provide a comprehensive understanding of accident but also benefit causes statistics. Although many accident cause classification models or taxonomies have been proposed, yet some models are domain-specific while others are too general or complicated for practical application. To address the basic two issues of accident analysis, which are (1) what is the failure and (2) how does the failure happen, a new model is presented from both system safety perspective and control theory perspective. First, complex systems can be decomposed into six components, which are machine, man, management, information, resources, and environment from the view of system safety factors. From control theory perspective, actuator, sensor, controller, and communication are defined as system factors’ functional abstractions. The combinations of system factors and control functions form a matrix model for accident causation analysis and classification, named Accident Causation Analysis and Taxonomy (ACAT) model. Then a comparison with existing cause classification schemes is made and the case of BP Texas refinery accident is used to illustrate its capability.

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