Abstract
Information modeling for complex electromechanical systems in the processing industry is the foundation for system vulnerability analysis, failure propagation mechanism, and fault root cause tracing driven by data analysis. Focusing on the difficulties in information modeling for complex electromechanical systems, a new approach based on interaction analysis and a general framework for information modeling of complex electromechanical systems are proposed. First, the basic structures of the information model are defined based on monitoring data. Second, an improved symbolic transfer entropy method with procedures for optimizing the number of symbols, phase space reconstruction, binary encoding, and decimal decoding are proposed to detect the interaction direction and quantify the interaction strength between different monitoring variables. Third, some optional methods are introduced to simplify and modify models. Finally, a directed-weighted information model for a specific complex electromechanical system is constructed based on information flow. An actual information modeling application of a complex electromechanical system is used to demonstrate the proposed method and compare it with existing methods. This new approach can handle general information modeling problems and overcome the drawbacks of existing methods since all the monitoring variables are used to improve integrity of the model, and prior knowledge about the physical structure and key points’ selection are not required. A unique and complete information model is obtained regardless of the choice of the initial variable. Thus, the proposed method can be flexibly and effectively used in information modeling for complex electromechanical systems in the processing industry and formulate the foundation of system vulnerability analysis, failure propagation mechanism, and fault root cause tracing methodology, as well as other engineering applications.
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