The importance of smart embedded systems in Industry 4.0

Luís Pires lpires@inete.pt INETE – Instituto de Educação Técnica

The development of Industry 4.0 is the combination of hardware and software into smart embedded systems will be greatly resting in Artificial Intelligence (AI) applications. However, sometimes those smart devices are generally referred to as a whole scale with blurred barriers, where it is difficult to establish when one element ends and the other starts. For example, during the first years of development of this concept, Internet of Things (IoT) was proposed to refer just to uniquely identifiable interoperable connected objects with Radiofrequency Identification (RFID) technology.

Later on, however, as the connectivity of these networks were getting bigger and including new technologies (Narrowband IoT – NB-IoT, LoRa, Wifi, ESP microcontrollers, Raspberry Pi, and others) and concepts, the term was growing with it to include all these innovations, applied to measure, identify, position, track and monitor objects, referring now to IoT more as a dynamic global network where self-conscious objects connect with each other.

In this new context where Cyber-Physical Systems (CPS) can be considered the proper “brains” inside industry 4.0, one way to establish some frontiers can be to consider IoT as the global framework where identification and sensor technologies become integrated with interpretation technologies like CPS [1].

robótica

8

artigo científico

The importance of smart embedded systems in Industry 4.0 1. CYBERPHYSICAL SYSTEMS Cyber-physical systems (CPS) are engineered systems that are built from and depend upon the synergy of computational and physical components [2]. Emerging CPS will be coordinated, distributed, and connected, and must be robust and responsive. CPS will transform the interaction of engineered systems, just as the Internet transformed the way people interact with information. In manufacturing, CPS can improve productivity and quality through smart prognostics and diagnostics utilizing big data from different networked sensors, machines, and systems. Each physical component and machine will have a twin model in cyber space. Each component and machine can predict and prevent potential failure and further with self-aware, self-predict, self-compare, and further self-reconfigure, and self-optimize for robust intelligence and performance.

2. TECHNICAL APPROACH The interface between the cyber space and the physical asset space cannot be realized without the proper platform and analytics technology. Keeping this in mind, this applies that the cyber representation of the automation system or asset is not a trivial task and requires advance learning algorithms and the use of historical embedded systems to achieve this accurate cyber representation. A high-level view of this cyber physical analytics platform with self-learning capabilities is illustrated in Figure 2, in which one must initially decide on

Manager & Operator

Sensor & Controller Network

Fleet

Sensor fusion Self-aware & .Adaptative control Self-maintenance .Self-diagnostic sensors

robótica 117, 4.o Trimestre de 2019

System

.Information distribution .Data management .Configurable application modules

Big Data & Cloud

Figure 1. Machines in Multi-dimensional Environment.

Reaserch Gap

.Human-machine Interaction .Maintenance Machine .Logistics .Peer-to-peer comparision .Knowledge accum .Similarity

Product & Quality

.Working regime normalization .Root cause reasoning .Stress based degradation

Machine Environment