Abstract
Emergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones.
Highlights
1.1 MotivationIn the agenda of Industry 4.0 (I4.0), resilience of cyber-physical systems is expected to be supported by monitoring and control capabilities provided by software components exposing an agile interface for integration and processing of data carrying information at different levels of granularity, according to various pillars, notably Industrial Internet of Things (IIoT), big data and analytics, simulation, horizontal and vertical integration, and cloud computing [42,46]
This paper presented a hardware/software framework, designed around a System of Systems (SoS) architecture, supporting resilience at runtime of cyber-physical systems, exploiting digital twins and failure models to improve operation, integration, maintenance, and recoverability for many application scenarios, notably including Smart City and Industrial Internet of Things contexts
The Knowledge Base developed around digital twins has been enriched with IoT control commands design with the aim of empowering the framework with remote actuation capabilities, enabling both recoverability and adaptability mechanisms in a proactive way: data analytic agents can perform failure detection and propagation analysis to infer disruptions and to react autonomously with resolutive actions or by activating chatbot agents for human interventions
Summary
In the agenda of Industry 4.0 (I4.0), resilience of cyber-physical systems is expected to be supported by monitoring and control capabilities provided by software components exposing an agile interface for integration and processing of data carrying information at different levels of granularity, according to various pillars, notably Industrial Internet of Things (IIoT), big data and analytics, simulation, horizontal and vertical integration, and cloud computing [42,46]. This gives raise to a class of software-controlled systems that can afford functional, structural, and behavioural complexity while still maintaining commitment for high levels of reliability [1,34,45]. Effective exploitation of this potential largely depends on architectural choices that shape integration between physical, hardware, software, and human operators, and by development practices that preserve reliability while increasing agility and complexity
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