FAR-EDGE Factory Automation Edge Computing Operating System Reference Implementation

Factory Automation Edge Computing Operating System Reference Implementation

Summary

Despite the proclaimed benefits (i.e. scalability, reliability, cost-effectiveness) of Future Internet (FI) technologies (i.e. edge & cloud computing, IoT/CPS) for factory automation, their adoption from manufacturers remains low for various reasons, including technology issues (e.g., poor situation awareness, limited deployments, no standards-based reference implementations) and the lack of a smooth migration path from legacy systems.

FAR-EDGE is a joint effort of leading experts in manufacturing, industrial automation and FI technologies towards the smooth and wider adoption of virtualized factory automation solutions based on FI technologies. It will research a novel factory automation platform based on edge computing architectures and IoT/CPS technologies. FAR-EDGE will provide a reference implementation of emerging standards-based solutions for industrial automation, along with simulation services for validating automation architectures and production scheduling scenarios.

FAR-EDGE will lower the barriers for manufacturers to move towards Industrie 4.0, as a means of facilitating mass-customization and reshoring. Emphasis will be paid in the study of migration options from legacy centralized architectures, to emerging FAR-EDGE based ones.

FAR-EDGE will be validated in real-life plants (VOLVO, WHIRLPOOL) in the scope of user-driven scenarios (business-cases) for mass-customization and reshoring, where tangible improvements relating to reliability, productivity increase, quality cost, reduction in adaptation effort/costs will be measured and evaluated. Also, a wide range of migration scenarios will be evaluated in the scope of a CPS manufacturing testbed. FAR-EDGE will also establish a unique ecosystem for FI factory automation solutions, which will bring together the FoF and FI communities and will ensure sustainability of FAR-EDGE results.

More information

Web resources:	http://www.faredge.eu https://cordis.europa.eu/project/rcn/205577/factsheet/en https://www.edge4industry.eu/
Duration:	37 months
Start date:	01-10-2016
End date:	30-10-2019
Number of participants:	12
Total budget - Public funding:	4 490 194,00 Euro - 3 992 631,00 Euro
Call topic:	Digital Automation - Novel architectures for factory automation based on CPS and IoT - Collaborative manufacturing and logistics (FoF.2016.11)
Instrument:	Collaborative project (generic)

Location

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Manufacturing system levels - (8)
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Manufacturing system levels

Manufacturing Cells

Enterprise - Factory

Significant innovations and lessons learned - (4)
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Significant innovations and lessons learned

Significant innovations and achievements

Comment:

FAR-EDGE demostrates the feasibility and business value of Edge Computing (EC) applied to manufacturing, using Distributing Ledger Technology (DLT) as the key enabler. DLT allows several autonomous local processes to cooperate as peers in the scope of the same global process, the required state synchronization and common business loging being implemented by Smart Contracts. This approach, if applied correctly, results in totally decentralized and fail-safe CPS that are still easily monitored, controlled and managed centrally.

Significance of the results for SMEs

Comment:

Adoption of new and disruptive technology is not an easy task for SME, due to lack of budget and of internal skills. To mitigate this problem, FAR-EDGE defines speficic migration strategies that may help SMEs plan their Industry 4.0 journey, with the support of FAR-EDGE assets.

Lessons learned

Comment:

In FAR-EDGE, the value of DLT is in distributed consensus: enabling the intelligent edge nodes of a system to agree - or disagree - on state transitions, to the effect that any change in global state (e.g., the assignment of a task to a specific node) must be approved collectively; moreover, individual nodes may fail or go offline without compromising the system as a whole. However, standard DLT platforms also maintain an immutable log of transactions (the Blockchain) that is replicated on all nodes, which may represent a significant performance bottleneck in many real-world applications. What we learned from the FAR-EDGE experimentation is that, in most factory automation scenarios, the historical memory of any transaction may be cleared once consensus is safely reached on it - something that is not supported by any current DLT implementation. This form of short-lived state persistency is something that is probably worth experimenting with in the future.

Specific use case requirements

Comment:

For being positively affected by the use of the FAR-EDGE Platform, use cases must have one or more of the following high-level requirements:

Distributed Automation: multiple (semi-)autonomous systems on the shopfloor having local resposibilities but global accountability.
Distributed Analytics: multiple local sources of raw data having high volume / velocity / variety characteristics (big data scenario).

Added value and impact - (3)
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Added value and impact

Manufacturing the products of the future

Customised products

Economic sustainability

Flexibility

Technologies and enablers - (9)
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Information and communication technologies

Comment:

The system blueprint is the FAR-EDGE RA. After having defined the requirements and the constraints for each block of the RA, a thorough analysis of the SotA has been done, which led to the identification of some existing software components meeting the specs. We then identified the gaps that the project will need to fill-in: not surprisingly, these where all the key enabling technologies, like the distributed ledger. However, hardly anything is going to be built totally from scratch in FAR-EDGE. The distributed ledger, for example, will be a customization of a generic, open source Blockchain platform (Hyperledger Fabric).

IoT - Internet of Things

Data analytics, processing and AI

Data storage

ICT solutions for next generation data storage and information mining

System modelling, simulation and forecasting

Programming Frameworks – Software Development Kits (SDKs)

FIWARE

Comment:

We are considering some core GEs from FIWARE as candidate building blocks of our Edge Computing architecture. In particular, the Publish/Subscribe GE (Orion Context Broker implementation) is a good candidate as the northbound interface exposed by Edge Gateways – i.e., computing nodes aggregating a number of local edge nodes (field devices, smart factory equipment) and running local automation and/or analytics processes. We are considering to significantly extend the Publish/Subscribe GE by adding distributed computing capabilities: a data context that is replicated and kept in-sync across a number of GE instances (running anywhere on the network), using a Blockchain and smart contracts as the backing technology. FAR-EDGE will contribute its results, as open source software, to the FIWARE for Industry community.

Results:

FAR-EDGE Architecture and Components Specifications

Smart Industry Context Information Management and Persistence

The Orion Context Broker Generic Enabler

Results:

FAR-EDGE Architecture and Components Specifications

Comment:

In the scope of FAR-EDGE, the value of FIWARE is in the OMA NGSI standard: a RESTful Web API implementing the publish/subscribe pattern on context information – i.e., a set of attributes representing the current state of some device or process. NGSI is the common language that FIWARE applications use to integrate themselves with the IoT world. For this reason, supporting NGSI in FAR-EDGE means opening up the Platform to the FIWARE community. The FIWARE asset that is crucial for the support of NGSI is Orion Context Broker (OCB), which as for all FIWARE Generic Enablers is open source software. In FAR-EDGE, we envision the use of OCB to implement the generic publish/subscribe interface of the Distributed Data Analytics subsystem

ICT Architectures

Comment: Relevant architectures for FAR-EDGE are Platform Industrie 4.0 (RAMI 4.0), Industrial Internet Consortium (IIRA), OpenFog Consortium (OpenFog RA). We will use the 3D coordinate system from RAMI 4.0 to map the elements of the FAR-EDGE Platform to concepts that are commonly understood and agreed on within the Industrie 4.0 community. We are also taking care that the FAR-EDGE Platform implementation will be compatible with future Industrie 4.0 compliant equipment – in particular I4.0 Components – by supporting OPC UA as the main protocol for field communication. Regarding IIRA and OpenFog RA, in the FAR-EDGE RA we are adopting a key architectural pattern included in both of them (with different names): a layered approach that leverages peer-to-peer communication in each layer to decentralize control. The most visible trait of the FAR-EDGE reference architecture is its partitioning into horizontal layers having different scopes – i.e., in bottom-up order: Fog (field, shopfloor), Edge (plant), Cloud (enterprise, supply chain). This pattern in inspired from a similar one in IIRA, called Layered Databus. However, in FAR-EDGE there is no “databus” included in each layer, but rather an additional layer called the Ledger, positioned between the Edge and the Cloud ones. Its role is to coordinate the execution of distributed processes running on Edge Gateways. The horizontal partitioning of the RA is driven by technical concerns (e.g., where software components are deployed, who are the stakeholders of their implementation). There is also a vertical partitioning, though, which is orthogonal to the horizontal one. This is a high-level functional decomposition which identifies three functional scopes: Automation, Virtualization and Analytics (more on that in the API section below).

Collaborative and decentralized application architectures and development tools

Digitalisation pathways - (5)
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Autonomous Smart Factories

Dedicated software in silos

Data acquisition/monitoring/analysis (SCADA) implemented but isolated

Comment:

The Analytics domain of the FAR-EDGE Platform is addressing data acquisition and analysis at the lowest level: optimizing the use of network and computing resources by applying Edge Computing patterns.

Connected IT and OT

ERP-MOM systems connected

Comment:

The Automation domain of the FAR-EDGE Platform introduced the concept of a Distibuted Ledger as an decentralized aggregation/coordination layer positioned between legacy ERP/MOM/MES systems (centralized control) and Edge Gateways (distributed analysis and execution), which in turn are aggregators of IoT-enabled field devices.

Off-line optimisation

Off-line Digital Manufacturing Process Optimisation on machine level

Comment:

The Virtualization domain of the FAR-EDGE Platform supports digital simulation, by means of which cyber-physical systems can be optimized following a what-if approach.

Platform enabled optimisation

Realtime optimisation

Autonomous /online/realtime Manufacturing Process Optimisation on factory level

ICT performance characteristics - (12)
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ICT performance characteristics

Data communication and interoperability

Comment: According to the RAMI 4.0 architecture, the “standard” way for Industrie 4.0 platforms to integrate legacy equipment (or any other kind of legacy “object”, including software components) into will be to encapsulate them inside an ad-hoc Administration Shell wrapper, which will expose them as I4.0 Components. The I4.0 interface specification has not been published yet, but a key enabling technology will probably be OPC UA, used as both a communication protocol and a data meta-model. In FAR-EDGE, OPC UA will be one of the field communication technology supported.

Platform level interoperability

AAA - Access, Authorisation and Authentication

Comment: TBD (OpenID Connect is the candidate technology for securing the Open API)

User Acccess and Rights Management

Comment: New Generation Access Control (NGAC) framework for the specificication and enforcement of access polocies

Application level interoperability

Modular Design and Deployment Approaches

Comment: The Platform is a composition of self-contained modules. Business logic for automation and analytics is packaged as Docker images.

Open APIs and Communication Protocols

Comment: The Plaform defines its own Open API

Integration level interoperability

Comment: AutomationML, SenseML, OPC UA.

Data/object model interoperability - Data exchange formats - APIs

Comment: The system blueprint is the FAR-EDGE RA. After having defined the requirements and the constraints for each block of the RA, a thorough analysis of the SotA has been done, which led to the identification of some existing software components meeting the specs. We then identified the gaps that the project will need to fill-in: not surprisingly, these where all the key enabling technologies, like the distributed ledger. However, hardly anything is going to be built totally from scratch in FAR-EDGE. The distributed ledger, for example, will be a customization of a generic, open source Blockchain platform (Hyperledger Fabric). Following the high-level functional decomposition defined in the FAR-EDGE RA, three Open APIs will be exposed by the Cloud layer of the FAR-EDGE Platform: Automation, Virtualization and Analytics. Automation will provide endpoints to monitor, control and manage automation workflows deployed on the Edge layer. Virtualization will create hooks for external simulation tools that need to read and manage factory models deployed on the Cloud layer (that are kept in-sync with the real world by the lower layers). Analytics will allow to manage distributed data analysis processes running on the Edge layer, and to collect aggregated results.

Connectivity & network interoperability – communication protocols

Comment: The FAR-EDGE architecture defines a protocol abstraction layer (the “connectivity middleware” box in the diagram provided here – this name is provisional) to decouple the field from its Edge Computing infrastructure: this is where OPC UA compatibility is going to be introduced.

Cybersecurity

Resilience

Scalability

Services

Standards, standardisation and regulation - (1)
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Standards

Comment: AutomationML, SenseML, OPC UA We expect OPC UA to gain more traction globally in smart factories, also independently from the standardization efforts of the Platform Industrie 4.0 initiative. It might indeed become, over time, the least common denominator of very diverse industrial systems all over the world.

Business model aspects - (2)
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Business model aspects of digital platform deployment

Service model

Comment: The FAR-EDGE RA, as well as the specifications of the FAR-EDGE Platform, will be open and royalty-free. We expect that other communities will be interested in the innovative concept of a distributed Ledger as an enabler for Edge Computing in industrial scenarios.

Software ownership

Results

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Demonstrators

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Organisations

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Contacts

Factory Automation Edge Computing Operating System Reference Implementation

Manufacturing system levels - (8) close

Significant innovations and lessons learned - (4) close

Added value and impact - (3) close

Technologies and enablers - (9) close

Digitalisation pathways - (5) close

ICT performance characteristics - (12) close

Standards, standardisation and regulation - (1) close

Business model aspects - (2) close

Mauro Isaja

Manufacturing system levels - (8)
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Significant innovations and lessons learned - (4)
close

Added value and impact - (3)
close

Technologies and enablers - (9)
close

Digitalisation pathways - (5)
close

ICT performance characteristics - (12)
close

Standards, standardisation and regulation - (1)
close

Business model aspects - (2)
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