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Fast prototyping of service robot behavior for a cleaning and tidying task in maintenance

The MANTIS project is concerned with predictive maintenance on the basis of big data streams from large (industrial) operations. At the end of the processing pipe line, planning suggestions for maintenance actions will be the result. Usually, maintenance is performed by human operators.

However, with current developments in machine learning, AI and robotics, it becomes interesting to see what type of ‘corrective actions’ in maintenance could be performed by industrial service robots.

In industrial production lines it is common to observe fairly short times between failure, especially in long chains. Whereas individual components are often designed to function extremely well, for instance under a regime of ‘zero-defect manufacturing’, the performance of the line as a whole may be disappointing. What is more, the actions performed by human operators to solve the problems may be very mundane and simple, such as removing dirt due to fouling or lubricating critical components. With the current advances in robot hardware and software technology, it becomes increasingly attractive to automate such maintenance actions. Whereas maintenance in the form of module- or part replacement are too difficult for current state-of-the-art robotics, cleaning and tidying is definitely possible.

With this application domain in mind, a laboratory setup was designed for quickly developing a robotic maintenance task for the purpose of demonstration by a master student team (Francesco Bidoia, Rik Timmers, Marc Groefsema) under guidance of a PhD student (Amir Shantia). We were able to realize a rapid configuration of our existing mobile robot platform to realize simple cleaning and tidying actions, similar to what is needed in basic industrial maintenance tasks. The demonstration involves speech control, navigational autonomy, work piece approach and dynamic reactivity to three object types, using tool switching. Objects are considered to be either a) untouchable, or b) removable by hand, or to consist, c) of small fragments (cf. ‘dirt’) that needs to be brushed away. In three weeks, a full demonstration could be developed by the student team, using a mobile robot with a single arm that was designed earlier, for Robocup@Home tasks:

The robot in our demonstration uses the light-weight carbon-fiberarm by Kinova (http://www.kinovarobotics.com/), a self-made transport base, standard Kinect sensors (for generating 3D point clouds) and digital cameras for vision. Programming was done using the ROS environment, with a pre-existing code base in C++ and Python.It is evident that by using current commercial existing mobile platforms such as KUKA (http://www.kukarobotics.com/en/products/mobility/KMR_iiwa/), MIR (http://mobile-industrial-robots.com/en/multimedia-2/videos/) and Universal Robots (https://www.universal-robots.com/), a similar, more sturdy industry-level system can be constructed.

Watch the whole demonstration here:

 

1st CREMA/C2NET Industrial Workshop

The 1st CREMA/C2NET Industrial workshop will take place the 24th November at Orona Fundazioa facilities located in Hernani (Basque Country – Spain). The event, organised by CREMA and C2NET H2020 EU projects, is intended to present future trends of European Industry especially those related to digitalization technologies applied to manufacturing. High levels speakers from the Basque Government, the European Commission, and the Industry sector (ill give their expert vision.

Moreover, CREMA and C2NET will present findings generated in both projects highlighting their approaches to meet above challenges. Presentations and practical demonstrations will be made by partners of both projects to present innovative solutions based on digital platforms in the Cloud to boost collaboration among manufacturing companies. Advanced Cloud technologies and applications will be shown to allow manufacturing companies faster and more efficient decision making for a better use of their manufacturing assets. Different business models and exploitation strategies followed by both projects to bring their outcomes to the market will also be presented.

Some MANTIS partners such as MGEP, IKERLAN, TEKNIKER, MCC, FAGOR ARRASATE and GOIZPER will attend this event to know other EU projects approaches to deal with common research areas and to make new contacts for potential collaboration actions in the future.

There is still time for registration accessing the website of the event: http://www.crema-c2networkshop.com/. We encourage you to do so.

Agenda:

09:00 – 09:30

Registration

09:30 – 09:40

Opening session

Welcome and event presentation

·       Eduardo Saiz, IK4-IKERLAN –  CREMA Project Researcher & C2NET Project Manager

Basque Government short talk

·       Alexander Arriola, General Director of SPRI/Basque Government

9:40 – 10:00

First Keynote: Industry 4.0 Implementation Strategy

·       Eduardo Beltrán, Innovation & Technology Director of MONDRAGON Corporation

10:00 – 10:15

Digitising European Industry

·       Max Lemke, Head of Unit Components and Systems, European Commission, DG CONNECT

10:15 – 11:00

The CREMA / C2NET viewpoint on future Industrial trends and a taste of the services that can be deployed in the Industrial Arena

·       Tim Dellas, ASCORA – CREMA Project Coordinator

·       Jorge Rodriguez, ATOS – C2NET Project Coordinator

11:00 – 11:30

Coffee Break

11:30 – 12:15

CREMA – Cloud Services for the Manufacturing Sector

·       Jon Rodriguez, FAGOR ARRASATE – CREMA Use Case I: Machinery Maintenance WP Leader

·       Mikel Anasagasti, GOIZPER – CREMA Use Case I: Machinery Maintenance Partner

·       Aizea Lojo, IK4-IKERLAN – CREMA Project Manager

·       Jessica Gil, TENNECO – CREMA Use Case II: Automotive WP Leader

12:15 – 13:00

C2NET – The complete Networked solution for Industry

·       Raúl Poler, UPV – C2NET Processes Optimization of Manufacturing Assets WP Leader

·       Carlos Agostinho, UNINOVA – C2NET Continuous Data Collection Framework WP Leader

·       Jacques Lamothe, ARMINES – C2NET Tools for Agile Collaboration WP Leader

13:00 – 14:00

Lunch break

14:00 – 14:45

Second Keynote: Industry 4.0 – How to master challenges to exploit new business opportunities

·       Stefan Zimmermann, Head of Global Vertical Manufacturing, Retail and Transportation market at ATOS

14:45 – 15:15

Interactive Session: Feedback from audience to capture the Pros and Cons if Industry 4.0. What hurdles need to be overcome  from an Industrial viewpoint

·       Moderator: Gash Bhullar, TANet – CREMA Impact WP Leader

15:15 – 15:45

CREMA / C2NET Response to the Interactive Session and potential solutions to the Industry 4.0 Implementation and Deployment Strategies

·       Moderator: Gash Bhullar, TANet – CREMA Impact WP Leader

15:45 – 16:00

Coffee Break

16:30 – 17:00

Panel discussion

·       Moderator: Gash Bhullar, CREMA TANet – Impact WP Leader

 

Closing remarks

·       Eduardo Saiz, IK4-IKERLAN –  CREMA Project Researcher & C2NET Project Manager

Presentation of the Mantis Project at Sirris’ seminar: fleet-based analytics for data-driven operation and maintenance optimization

On October 24th Sirris organized an industrial seminar on the opportunities and challenges related to fleet-based data exploration. During this seminar, a general introduction to the MANTIS project was first given, followed by presentations from several partners within theMANTIS project including: the Mondragon University (press machines), the Eindhoven University of Technology (shaver manufacturing), 3E (Photovoltaic Plants), Ilias Solutions (Vehicles), Atlas Copco (compressors) and Sirris. The event was a real success with around 45 participants and offered participants via real-world use cases in the different industrial domains mentioned above the opportunity to see how data-driven analytics on a fleet of machines can optimize the operation and maintenance of those.

Sirris presentation MANTIS
Tom Tourwe introduced the MANTIS project

 

Mondragon presentation MANTIS
Urko Zurutuza presented the Press Machine Maintenance Use-Case

If you would like to have further information on the outcomes of this seminar, please contact Caroline Mair (caroline.mair@sirris.be)

Deep learning for predictive maintenance

There are two extreme approaches to predicting failures for predictive maintenance. The white box approach relies on manually constructed physical and mechanical models for predicting the failures. The black box approach, on the other hand, relies on failure prediction models constructed using statistical and machine learning methods based on the data gathered from a running system. The figure below illustrates such data driven failure prediction for a machine monitored by three sensors.

data driven failure prediction
data driven failure prediction

Machine learning algorithms are used to identify failure patterns in the sensor data that precede a machine failure. When such patterns are observed in operation, an alarm can be triggered to take corrective action to prevent or mitigate the eminent failure. For example, failure predictions can be used to optimize the maintenance actions, such as scheduling the service engineers or managing the spare parts storage to reduce the downtime cost.

Automatic feature extraction

An important part of modeling a failure predictor is selecting or constructing the right features, i.e. selecting existing features from the data set, or constructing derivative features, which are most suitable for solving the learning task.

Traditionally, the features are selected manually, relying on the experience of process engineers who understand the physical and mechanical processes in the analyzed system. Unfortunately, manual feature selection suffers from different kinds of bias and is very labor intensive. Moreover, the selected features are specific to a particular learning task, and cannot be easily reused in a different task (e.g. the features which are effective for predicting failures in one production line will not necessarily be effective in a different line).

Deep learning techniques investigated in the MANTIS project offer an alternative to manual feature selection.  It refers to a branch of machine learning based on algorithms which automatically extract abstract features from the raw data that are most suitable for solving a particular learning task. Predictive maintenance can benefit from such automatic feature extraction to reduce effort, cost and delay that are associated with extracting good features.

Sirris seminar on fleet-based analytics for data-driven operation and maintenance optimization

On October 24th Sirris is organizing in Belgium an industrial seminar on the opportunities and challenges related to fleet-based data exploration. During this event Belgian as well as other European industrial partners from the MANTIS project will present their experience with fleet-based analytics (based on use-cases from the MANTIS project).

Many companies operate a fleet of machines that have a similar, almost identical behaviour in terms of internal operation, application and usage, such as for example windmills, compressors and professional vehicles. This set of almost identical machines is defined as ‘a fleet’.

In addition, more and more, those machines are equipped with several (smart) sensors, that can capture data on operational temperature, vibrations, pressure and many other features, depending on the machine. In addition, the communication and data storage technologies are becoming ubiquitous, making it possible to gather the data in a central platform and derive insights into normal and anomalous behaviour across the entire fleet of machines. By comparing for example the behaviour of a single machine to the rest of the fleet, one can identify if a machine is underperforming due to misconfiguration or imminent failure. The analysis of this data can also help service and maintenance personnel to have a more detailed and optimised maintenance planning, e.g. ensuring an optimal distribution of the entire fleet in terms of remaining useful life, in order to manage the work load of the service engineers. Therefore, the exploitation of the data collected on a fleet of machines is a real asset for maintenance and service personnel and, at a larger scale, for an entire company.

You are interested in this event? Check out the event’s agenda and register here

Programme

13:00 – 13:15: Registration and coffee

13:15 – 13:30: Setting the scene (Sirris)

13:30 – 14:30: MANTIS project: Cyber Physical System based Proactive Collaborative Maintenance

    • Project goals and challenges by Sirris
    • Fagor use case (press machines) – title to be announced
    • Philips use case (shaver manufacturing) – title to be announced

14:30 – 15:30: Root cause analysis

    • Barco – Vitriol: let open source data science talk quality and business at Barco Projection
    • 3E – Data-driven Fault Detection for Photovoltaic Plants: Data Quality, Common Faults and Data Annotation
    • Atlas Copco – SMARTLINK & root-cause analysis on compressors worldwide to improve on operational efficiency

15:30 – 15:50: Coffee break

15:50 – 17:10: Failure prediction & Operational optimisation

    • Barco – LightLease Predicting Lamp Behaviour in Digital Cinema
    • Ilias – Towards Predictive Vehicle Fleet Management
    • Maintenance Partners – Performance optimisation and failure prediction of wind turbines
    • Pepite – Analytics for operational optimisation

17:10 – 17:30: Closing remarks (Sirris)

17:30 – Networking reception

MANTIS-Platform Requirements or How to Make Sure the Platform Fulfils Partner’s Needs

One of the most important tasks to ensure flawless working on different work packages is to have fully consolidated requirements towards the MANTIS platform. Not only is it important to have all different requirements aggregated, but also to know in which project tasks those requirements will be addressed, by whom and how to manage those during the project lifetime. To handle this challenge with excellence, MANTIS partners decided to do a 4-step approach in requirement definition towards the MANTIS platform.

Defining User Scenarios & Deriving Requirements

In the MANTIS project, we do have specific use cases we later on want to test the platform against. Each of these use cases detailed their scenarios and related them to the MANTIS objectives. From there on, requirements in the form of a table were compiled. Those requirements cover functional, non-functional, technological and business needs. As a result, we had the first form of requirements which – of course- still needed unification and consolidation.

Extending Maintenance Use Case Requirements by MANTIS Partner Requirements

This task focused on what technology can and should be used for the user scenarios. The main task members therefore were the technology providing partners. We also expected requirements originating from technology push of the providers. Those additional requirements were the second form of requirements we had which – again, of course – still needed unification and consolidation. Nonetheless, correlations to the user scenario requirements were already identified and noted.

Requirements and Project Objects Consolidation

The platform requirements and the user scenario requirements were revisited for consistency and updates. Furthermore, a refinement of the requirements was performed based on the early sketches of the MANTIS architecture. As a result, 45 different requirement categories were identified, 27 of those categories were then identified as “not to be addressed inside of MANTIS”, since those categories mainly were basic requirements towards a general platform architecture and not MANTIS specific.

 

MANTIS requirements categories
MANTIS requirements categories

 

Refinement of the Requirements

As the last task, which is currently still running strong, we wanted to match all those 900+ requirements that were identified and defined to the 45 defined requirement categories. At the time of writing this article, the matching is completed and 900 requirements could be reduced to about 330 MANTIS specific requirements. Also, the MANTIS specific requirement categories were matched to the different project task, to make sure that the requirements will definitely be addressed.

So what’s ahead: Definition of MANTIS platform requirements is near its end. What’s left to do is to choose a good way to manage the requirements during projects lifetime. As it seems, this will be done by separating Excel sheets according to tasks and categories. This will keep management of requirements handy and easy.

 

Matching Requirements to MANTIS requirement categories
Matching Requirements to MANTIS requirement categories

An integrated Mantis-Platform for Off-Road and Special Purpose Vehicles

Introduction

Off-Road and Special Purpose vehicles are used all over the world in various environmental conditions. They exist in all different kinds and formats and, within companies, a broad range of types of such vehicles will be in use.

Maintenance on these vehicles, be it preventive or corrective, can cause unavailability, having a negative impact on both productivity and efficiency. An overall objective regarding maintenance is to maximize the availability of the vehicles at the lowest maintenance cost. Therefore, a pro-active and preventive maintenance approach should lead to important savings, with higher availability.

The ILIAS approach

Most of these vehicles are already equipped with on-board HUMS systems/black boxes. The data generated by these on-board systems contain a broad range of information that can be used as input in a MANTIS-based platform in order to optimize the full maintenance strategy.

The diversity of HUMS systems, however, is very broad, even on the same type of vehicles. Each vehicle has its own configurations, interfaces, data formats, etc. Hence, there is a need to convert the collected data from various systems into a uniform and structured format in order to make them further exploitable.

This observation has led us to the conclusion that there are two viable approaches to building MANTIS-based platforms:

  • A per-vehicle type / HUMS system platform approach, aiming at an optimum maintenance strategy for a small number of equipment types.
  • An open platform approach that can be easily customized by the user to the type of vehicle/HUMS system(s) being used.

We opted for the second approach but have not limited it to the collection of data only but broadened it to a complete set of functionalities within the MANTIS-based platform.

Based on the experience and know-how gained from the collaboration within the MANTIS project and the architectural guidelines derived from it, ILIAS Solutions aims at building a platform that provides a complete solution from the readout of the black box until the optimization of the maintenance plans in an environment with high numbers of highly complex and mobile assets.

The ILIAS platform, therefore, provides users with an integrated set of user-friendly tools, permitting them to:

  • Import data from external sources like ERP systems, leading to a centralized data set. (Step 1)
  • Import raw data coming from any kind of HUMS system and cleanse them, based on automatic data wrangling, leading to state detection and health assessment. (Steps 2, 3, 4)
  • Make analysis of the data via different algorithms and translate them into rules/conditions to apply in the system. (Steps 5)
  • Define rules/conditions, including use and abuse rules, for triggering maintenance or other linked actions, based on the combined dataset. (Step 6)
  • Approve/disapprove the system-proposed maintenance actions and register them to make the system self-learning. (Steps 7, 8, 9)

This figure illustrates the approach.

MANTIS Approach proposed by ILIAS as a maintenance platform in Off-Road and Special Purpose vehivles
MANTIS Approach proposed by ILIAS as a maintenance platform in Off-Road and Special Purpose vehivles

The figure below illustrates how we go through different steps in implementing the platform, following more iterations to improve the system.

ILIAS is running different steps in implementing the platform
ILIAS is running different steps in implementing the platform

Conclusion

For Off-Road and Special Purpose vehicles, the overall objective regarding maintenance is to maximize the availability of the vehicles at the lowest maintenance cost. Thus, a proactive and preventive maintenance approach leads to important savings, with higher availability.

ILIAS Solutions aims at building a platform that provides a complete solution from the readout of the black box until the optimization of the maintenance plans in an environment with high numbers of highly complex and mobile assets.

This platform should be an open platform that can be easily customized by the user to the type of vehicle/HUMS system(s) in use and where a number of rule sets/conditions are defined in a user-friendly way.  This allows the system to trigger predictive maintenance actions. Analysis of broad data sets will lead to additional rules and conditions, optimizing the platform it selves.

Closure event for T-Rex European project: WorkShop “Industry 4.0: Extension of machinery life-cycle, component re-use and servitization”

T-Rex project – Lifecycle Extension Through Product Redesign And Repair, Renovation, Reuse, Recycle Strategies For Usage & Reusage-Oriented Business Models

In current global economy, manufacturers are under pressure to adapt to an ever-changing business environment. As a consequence, as part of Industry 4.0, new trends are gaining momentum, such as the servitization of manufacturing. The servitization can also be seen as a business model innovation of organization process and capabilities, where service-oriented activities increase. This leads in turn to revise the importance of certain strategies and technologies, such as reliability and life-cycle assessment, service engineering or advanced maintenance.

Previous to the MANTIS project, during last 3 years, T-REX project, funded under 7th framework factories of the future programme, has developed technologies oriented to the extension of machinery life-cycle, component re-use and servitization. Moreover, it has developed a framework and other support tools to facilitate new business opportunities to companies, in particular SMEs.

These activities are all contributing towards the development of Industry 4.0, in particular with respect to the extension of the manufacturing activities beyond the factory.

Ulma truck and screenshot of the trucks fleet Control panel at the BIEMH fair in Bilbao (June 2016)
Ulma truck and screenshot of the trucks fleet Control panel at the BIEMH fair in Bilbao (June 2016)

Workshop “Industry 4.0: Extension of machinery life-cycle, component re-use and servitization”

As closure event for the T-Rex project in September 2016 IK4-TEKNIKER and ULMA are organizing a workshop which aims to demonstrate the feasibility of service-oriented business models, in particular for SMEs, and will include direct feedback from manufacturing companies interested in extending servitization and re-use activities. Part of these activities will take advantage of the results obtained in T-REX project.

WorkShop programme and registration

S[&]T corporation: Your partner in smart manufacturing

Science and Technology BV (S[&]T) is a SME developing cutting edge technology for complex systems. The company has built track record in space industry and since several years it is applying its innovation knowledge in smart manufacturing. Examples are central use of predictive analytics for real-time optimization and automated event handling with intelligent database and self-learning algorithm enabling impact analysis and decision support.

Below are some examples of previous projects that helped build the knowledge base:

Decision Support Systems

Time is a scarce and expensive resource aboard challenging scientific environments such as the International Space Station (ISS). The training and preparation of astronauts for onboard missions can take up a large amount of this resource, as do the actual maintenance, operation and troubleshooting involved with such missions. ESA has been researching more efficient, effective, and easy ways to realize human operations. S[&]T works on the following projects: ETECA (Expert Tool to Enhance Crew Autonomy), MECA (Mission Execution Crew Assistant) and TIDE (Technology for Information, Decision and Execution superiority). These technologies form the basis of decision support development in MANTIS.

GUI of Mission Execution Crew Assistant
GUI of Mission Execution Crew Assistant

System Health Management

SHM optimizes operation of complex systems by analyzing their health using either model-based methods i.e., using online sensor data and knowledge of normative system behaviour, or data-driven techniques, i.e., system behaviour is learned and extracted from large data volumes. Typical functionality includes: fault detection, diagnosis of system failures, and prediction of system failures. S[&]T has been involved in: ESA’s Future Launchers Preparatory Programme (FLPP), the real-time multiple sensor array LOFAR, and the development of a health management system for a Reusable Space Transportation System.

Rocket propellant with graphical analysis of the system health management
Rocket propellant with graphical analysis of the system health management
An example of system health management
Rocket propellant with graphical analysis of the system health management

Aim of S[&]T is to further develop its digital factory tools in a modular way. In this way specific analysis- and decision-support solutions can be created, dedicated per client.

Emerging Requirements for Future Manufacturing: the role of the new IoT/CPS based technologies

Internet of Things Applications in Future Manufacturing

The book chapter “Internet of things Applications in Future Manufacturing” is part of the 2016 IERC-European Research Cluster on the Internet of Things book (Digitising the Industry – Internet of Things Connecting the Physical, Digital and Virtual Worlds). This book is published by “River Publishers Series in Communications” that is a series of comprehensive academic and professional books which focus on communication and network systems. The book chapter is the result of a collaboration between John Soldados (Athens Information Technology), Sergio Gusmeroli (Politecnico di Milano) and MANTIS partner: Pedro Malo and Giovanni Di Orio (UNINOVA). You can download the book chapter in the Dissemination section of the web as well as the entire book by using the following link:

http://www.internet-of-things-research.eu/pdf/Digitising_the_Industry_IoT_IERC_2016_Cluster_eBook_978-87-93379-82-4_P_Web.pdf

 

Introduction — Future manufacturing is driven by a number of emerging requirements including:

  • The need for a shift from capacity to capability, which aims at increasing manufacturing flexibility towards responding to variable market demand and achieving high-levels of customer fulfillment.
  • Support for new production models, beyond mass production. Factories of the future prescribe a transition from conventional make-to-stock (MTS) to emerging make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO) production models. The support of these models can render manufacturers more demand driven. For example, such production models are a key prerequisite for supporting mass customization, as a means of increasing variety with only minimal increase in production costs.
  • A trend towards profitable proximity sourcing and production, which enables the development of modular products based on common plat- forms and configurable options. This trend requires also the adoption of hybrid production and sourcing strategies towards producing modular platforms centrally, based on the participation of suppliers, distributors and retailers. As part of this trend, stakeholders are able to tailor final products locally in order to better serve local customer demand.
  • Improved workforce engagement, through enabling people to remain at the heart of the future factory, while empowering them to take efficient decisions despite the ever-increasing operational complexity of future factories. Workforce engagement in the factories of the future is typically associated with higher levels of collaboration between workers within the same plant, but also across different plants.

The advent of future internet technologies, including cloud computing and the Internet of Things (IoT), provides essential support to fulfilling these requirements and enhancing the efficiency and performance of factory processes. Indeed, nowadays manufacturers are increasingly deploying Future Internet (FI) technologies (such as cloud computing, IoT and Cyber-Physical Systems (CPS) in the shop floor. These technologies are at the heart of the fourth industrial revolution (Industrie 4.0) and enable a deeper meshing of virtual and physical machines, which could drive the transformation and the optimisation of the manufacturing value chain, including all touch-points from suppliers to customers. Furthermore, they enable the inter-connection of products, people, processes and infrastructures, towards more automated, intelligent and streamlined manufacturing processes. Future internet technologies are also gradually deployed in the shopfloor, as a means of transforming conventional centralized automation models (e.g., SCADA (Supervisory Control and Data Acquisition), MES (Manufacturing Execution Systems), ERP (Enterprise Resource Planning)) on powerful central servers) towards more decentralized models that provide flexibility in the deployment of advanced manufacturing technology.

The application of future internet technologies in general and of the IoT in particular, in the scope of future manufacturing, can be classified in two broad categories:

  • IoT-based virtual manufacturing applications, which exploit IoT and cloud technologies in order to connect stakeholders, products and plants in a virtual manufacturing chain. Virtual manufacturing applications enable connected supply chains, informed manufacturing plants comprising informed people, informed products, informed processes, and informed infrastructures, thus enabling the streamlining of manufacturing processes.
  • IoT-based factory automation, focusing on the decentralization of the factory automation pyramid towards facilitating the integration of new systems, including production stations and new technologies such as sensors, Radio Frequency Identification (RFID) and 3D printing. Such integration could greatly boost manufacturing quality and performance, while at the same time enabling increased responsiveness to external triggers and customer demands.

Within the above-mentioned categories of IoT deployments (i.e. IoT in the virtual manufacturing chains and IoT for factory automation), several IoT added-value applications can been supported. Prominent examples of such applications include connected supply chains that are responsive to customer demands, proactive maintenance of infrastructure based on preventive and condition-based monitoring, recycling, integration of bartering processes in virtual manufacturing chains, increased automation through interconnection of the shopfloor with the topfloor, as well as management and monitoring of critical assets. These applications can have tangible benefits on the competitiveness of manufacturers, through impacting production quality, time and cost. Nevertheless, deployments are still in their infancy for a number of reasons including:

  • Lack of track record and large scale pilots: Despite the proclaimed benefits of IoT deployments in manufacturing, there are still only a limited number of deployments. Hence manufacturers seek for tangible showcases, while solutions providers are trying to build track record and reputation.
  • Manufacturers’ reluctance: Manufacturers are rather conservative when it comes to adopting digital technology. This reluctance is intensified given that several past deployments of digital technologies (e.g. Service Oriented Architectures (SOA), Intelligent Agents) have failed to demonstrate tangible improvements in quality, time and cost at the same time.
  • Absence of a smooth migration path: Factories and production processes cannot change overnight. Manufacturers are therefore seeking for a smooth migration path from existing deployments to emerging future internet technologies based ones.
  • Technical and Technological challenges: A range of technical challenges still exist, including the lack of standards, the fact that security and privacy solutions are in their infancy, as well as the poor use of data analytics technologies. Emerging deployments and pilots are expected to demonstrate tangible improvements in these technological areas as a prerequisite step for moving them into production deployment.

In order to confront the above-listed challenges, IoT experts and manufacturers are still undertaking intensive R&D and standardization activities. Such research is undertaken within the IERC cluster, given that several topics dealt within the cluster are applicable to future factories. Moreover, the Alliance for IoT Innovation (AIOTI) has established a working group (WG) (namely WG11), which is dedicated to smart manufacturing based on IoT technologies. Likewise, a significant number of projects of the FP7 and H2020 programme have been dealing with the application and deployment of advanced IoT technologies for factory automation and virtual manufacturing chains. The rest of this chapter presents several of these initiatives in the form of IoT technologies and related applications. In particular, the chapter illustrates IoT technologies that can support virtual manufacturing chains and decentralized factory automation, including related future internet technologies such as edge/cloud computing and BigData analytics. Furthermore, characteristic IoT applications are presented. The various technologies and applications include work undertaken in recent FP7 and H2020 projects, including FP7 FITMAN, FP7 ProaSense, ECSEL MANTIS, H2020 BeInCPPS, as well as the H2020 FAR-EDGE initiative. The chapter is structured as follows: The second section of the chapter following this introduction illustrates the role of IoT technologies in the scope of EU’s digital industry agenda with particular emphasis on the use of IoT platforms (includ- ing FITMAN and FIWARE) for virtual manufacturing. The third section is devoted to decentralized factory automation based on IoT technologies. A set of representative applications, including applications deployed in FP7 and H2020 projects are presented in the fourth section. Finally, the fifth section is the concluding one, which provides also directions for further research and experimentation, including ideas for large-scale pilots.