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Modelling CECA Diagram as a State Machine

Abstract : Cause-Effect Chains Analysis (CECA) is one of the main TRIZ methods used for identification of system disadvantages. The analysis results in a diagram documenting these disadvantages and causal relations between them. Although the nature of causality implies that any effect must follow its cause, the original CECA concept does not address time explicitly. This drawback has been indicated by Yoon, who proposed Occasion Axis to describe changes of system state upon meeting particular conditions specified using values of parameters. Such an axis illustrates a sequence in time and an additional requirement is to interleave nodes referring to parameters with those referring to functions, originally dubbed as Parameter-Function Pair Nexus.The method of transforming a CECA diagram into a logical model presented during TFC 2016 conference relies on decomposing the diagram into a context-dependent layer (specific content) and a context-independent layer, representing the structure of connections. The logical model describes the structure with a set of Boolean functions, which may be minimized and analyzed in a systematic way.This paper explores the idea of Occasion Axis and examines the possibility of converting a CECA model into a state machine with transitions between the states described by conditions referring to parameters of objects in the analyzed system or its super-system. The expected benefits of such transformation range from better understanding of the time-domain interrelations of the causes up to describing the causality using standard notation, such as UML. The paper presents rules proposed for systematic conversion of CECA diagram into state machine representation and discusses required extensions to formal state machine definition.
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Submitted on : Thursday, September 5, 2019 - 3:17:32 PM
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Jerzy Chrząszcz. Modelling CECA Diagram as a State Machine. 18th TRIZ Future Conference (TFC), Oct 2018, Strasbourg, France. pp.302-314, ⟨10.1007/978-3-030-02456-7_25⟩. ⟨hal-02279776⟩



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