A framework for Model-Driven Engineering of resilient software-controlled systems

AbstractEmergent 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

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

Benefits of industry 4.0 in foundry engineering’s greensand moulding process

Abstract: There is a need to introduce modern technologies to address inefficiencies in foundry engineering. The foundry industry is very old dating back as far as 1479. The early foundry engineers produced metal castings which were mainly cannons and bells. Foundries have been slow to adapt to disruptive technologies. However with the 4th industrial revolution foundries cannot afford to miss out. Foundry Engineering which is metal casting is under a lot of pressure from other competing manufacturing technologies. Forging, fabrications and 3D metal printing, plastic and composite materials are competitors to metal casting. The most common and cheapest way of producing castings is in greensand. This is due to the fact that it uses low cost raw materials. Though the process is cheaper than other casting processes. There is always a need for improving efficiencies in the means of production to compete with other manufacturing technologies. The 4th industrial revolution has become a pillar of improving competiveness in the metal casting process. This paper evaluates how the first cloud based green sand data analytic software Sandman plays a role in contributing towards the achievement of the sustainable development goals in African foundries. The greensand data analytic programme has been seen to be a key resource in driving for responsible consumption and production. This study will provide knowledge on the benefits of using a data analytic software in greensand moulding