Reconceptualizing Industrial Measurement and Control Networks

Abstract

 Driven by the growing needs of high-end equipment manufacturing, complex system verification, and distributed testing, industrial networks are moving beyond device connection and data communication to support deterministic control, real-time data exchange, and coordinated testing. Although deterministic communication technologies have improved time-critical transmission, local synchronization, and priority control, existing industrial control networks still face limits in wide-area distributed testing, multi-source synchronous acquisition, transient event capture, flexible scheduling, and dynamic adaptation. To address these challenges, the evolution of industrial networks is reviewed, and the fundamental differences between test-and-measurement tasks and logic-control tasks are compared in terms of service features, trigger modes, time references, and system boundaries. On this basis, a conceptual framework for industrial measurement and control networks (IMCN) is proposed. The framework defines ten core capabilities: synchronous execution, synchronous acquisition, precise time capture, strict priority assurance, sensor network support, deeply embedded built-in testing, adaptive multi-source timing, dynamic topology reconstruction, network-shared storage, and a native networked instrumentation bus architecture. It also highlights the connotations in four aspects: inheritance, testability, traceability, and autonomy. The applicability and practical value of the proposed conceptual framework are further illustrated through the case of SharkNet in aerospace and other high-end measurement and control systems, while its potential significance for self-organizing, reconstructible, and brain-inspired physical network architectures is also discussed.