Research and Application of Functional Model Construction Method for Production Equipment Operation Management and Control Oriented to Diversified and Personalized Scenarios

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Abstract
As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality.
Funding Information
  • National Key Research and Development Program of China (2021YFB1715300)
  • National Natural Science Foundation of China (62172425)

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