October 2015

Consorzio Interuniversitario Nazionale per l’Informatica (CINI)

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CINI (Consorzio Interuniversitario Nazionale per l’Informatica) is the main reference for the national research in Computer Science and Information Technology. CINI is a consortium founded in the 1989 by nine universities with headquarters in Rome and under the supervision of the Ministry of University and Research. At present, 36 universities participate to the consortium. CINI is organised in local operating units and in national research areas covering all the different aspects of IT. At present, it includes 1,278 professors and researchers in Computer Science related areas. It is distributed across the entire country, the headquarters are in Rome. CINI promotes and coordinates: frontier and industrial research in Computer Science, technology transfer and scientific activities in collaboration with universities, research centres, and companies.

Relevant Expertise for the project:

CINI is a very active organisation at National and EU level. It participates to several national and international projects. At present, there are projects funded by several entities including: EU (ARTEMIS, FP6, and FP7), Italian Ministry of University, Italian Public Administrations, private companies, etc. It participates to the planning of national research activities (e.g., PNR 2011-2013) and to European research initiatives in ITC (e.g., the NESSI technology platform). It provides support to the associations of university professors and PhD schools. It collaborates with companies in applied research activities and technology transfer in the ICT area.

Role in the project:

Systems reliability is always difficult to estimate properly due to a number of factors that can affect the results of the predictive models used. Therefore, even if such models are important for a proper planning, they are not enough and they can be enhanced using up-to-date and continuous information flows that can be collected from the overall system at run-time. Such additional knowledge can be used to tune continuously ad-hoc models to enhance their prediction capabilities. Moreover, in a connected world, such information can also be shared with similar systems deployed in comparable environments to further improve the prediction abilities based on a larger set of data. However, the definition of reliable models cannot be done only on the base of theories, it require a continuous experimentation process to verify the effectiveness of the model in real conditions and a related tuning.

The contribution of CINI will focus on the data analysis of information flows coming from different sources to take proper decisions at different times (WP4):

  • At design time: use already available information from existing systems to create predictive models that can be adapted and used to support the development and/or the maintenance of a systems.
  • At run-time: use of on-line data collection and analysis techniques to make short-term and longterm predictions on the status of a working system to plan maintenance properly. Such models will be adaptive, they will be able to learn from the operating conditions of the single system under investigation and from other similar systems deployed in similar conditions. Moreover, CINI will contribute to the development of the reference framework (WP2) integrating the abilities described and supporting the definition of common application scenarios.

Ansaldo STS S.p.A. (ASTS)

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Ansaldo STS is a public company, listed on the Milan Stock Exchange, the Italian Stock Exchange, on STAR segment, with subscribed and paid in capital amounted to e90,000,000.00. Ansaldo STS is a leading company operating in the sector of high technology for railway and urban transport. The Company operates in the design, implementation and management of embedded safety critical systems and services for signalling and supervision of railway and urban traffic, as well as lead contractor. Ansaldo STS is headquartered in Genoa and has more than 4,100 employees in more than 30 countries. The 40% of the share capital is held by Finmeccanica shareholder. The company has an international geographical organisation present in: Central and Eastern Europe and the Middle East, Western Europe and North Africa, the Americas, Asia and the Pacific. The company operates worldwide as lead contractor, system integrator and supplier ”turnkey” of the most important projects of mass transportation in metro and urban railways. The various Legal Entities included in the company perimeter play activities in the fields of traffic management and control of trains, production of signalling systems and maintenance services, in the interests of efficiency and safety over time for both clients and for end users. Depending on the customer’s request or on the type of order, Ansaldo STS is able to operate in either the Railway or Urban Transport Systems market as either:

  • a provider of ”turnkey” solutions as a General Contractor (also managing the civil works required to implement the project) and Integrated Transport Systems with its Signalling products
  • or independently providing only the technological part and the Signalling products or the engineering expertise of the IT system engineer/technology integrator or, partially providing other subsystems.

Furthermore, in the market of Signalling, Ansaldo STS provides components and services, either sold with the signalling and control system or separately. In the market of Transportation Solutions Ansaldo STS provides operation and maintenance services for the Urban Transport Systems. Ansaldo STS designs and produces integrated transport systems, i.e. it studies. designs and plans the integration of the design and construction of the technological elements that make up the transport system, including equipment, signalling, power supply, telecommunications and rolling-stock (whether for railway or metro trains), as well as any other technical items that, together with the foregoing, constitute an integrated transport system. The end product, i.e. an integrated transport system, whether railway line or metro line, is then delivered to the principal on a turnkey basis. The Group is also able to offer signalling and transportation systems competencies separately according to the client’s needs. Ansaldo STS is one of the Founding Members of the Shift2Rail Joint Undertaking.

Relevant Expertise for the project:

Ansaldo STS has been involved in several Artemis JU projects aimed at increasing process efficiency. In particular it was involved in CESAR project (Cost-Efficient Methods and Processes for Safety Relevant Embedded Systems), MBAT (Combined Model-based Analysis and Testing of Embedded Systems) and CRYSTAL (Critical System Engineering Acceleration). All of them are addressed to develop test architectures and tools that Ansaldo STS applied to its safety related use cases achieving valuable impacts on its huge testing processes. MANTIS is an opportunity to improve our maintainability process basing on trustable assumptions in terms of hardware reliability

Role in the project:

In the scope of ASTS is the management and prevention of the fault via an HW design that assure an high time to the failure. One of the most relevant role will be played in addressing the use of a Reliability Test strategy, based on mathematical and theorical assumption, in order to validate the MTBF value (coming from predictive analysis) with a fixed confidence interval. In a few words, ASTS role will be contributing to develop new approaches about fault and validate them in a specific rail use case. On the basis of the role above defined, ASTS will contribute to define the Framework requirements providing inputs specific for the rail domain. The defined methodological approach will be first of all specialised in the rail environment and then a use case scenarios will be defined and implemented in a demonstrator in order to validate the developed methodology

ADIRA – Metal Forming Solutions S.A. (ADIRA)

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ADIRA designs, develops, manufactures and installs state-of-the-art machine tools for more than 55 years, being a leading manufacturer and global supplier of sheet metal working machinery, specialised in the production of laser cutting machines, hydraulic press brakes, shears, robotised bending cells and automatic sheet metal transforming systems. ADIRA also supply complete solutions for the sheet metal processing industry, including materials such as stainless steel, steel, aluminium, brass, etc. Presently ADIRA exports to more than 50 countries worldwide, having more than 75% of its turnover coming from outside its homeland. ADIRA has the biggest market share in Portugal. The key for ADIRA success in the market is its great flexibility in producing customised products and its bet in offering innovative and high quality products.

ADIRA is known for:

  • Permanent investment in scientific research and technological development to supply at all times the best solutions to customers
  • Cooperation with Universities and renowned Research Labs
  • Quality, precision and reliability of solutions
  • Maximum performance products
  • Readiness and effectiveness of the after-sales service.

The commitment of ADIRA to innovation and continuous improvement is a permanent challenge of all its departments, products and activities. Also safety, ergonomics, efficiency and design are permanently on the requirements list for all new machines. ADIRA Engineering Department is closely linked to Universities and Development Institutes (University of Porto, MIT, INEGI, INESC Porto and others) in a network of knowledge transfer, taking advantage of the most advanced developments.

Relevant Expertise for the project:

ADIRA has an experience of many years in designing, manufacturing, using and servicing machine tools. As a user but mainly as a manufacturer, maintenance plays an important role in machine tools business. ADIRA has, therefore, a strong and experienced service and engineering teams working together to provide state of the art solutions in what relates to maintenance. The knowledge of the machines produced and their possible behaviour is of major importance in the definition of the models for the predictive maintenance and for defining the traditional maintenance approaches. The knowledge acquired as a maintenance services provider will help in designing the tools to be developed. ADIRA has participated in several R&D projects. These projects have originated state of the art products in all ranges of ADIRA portfolio. The partnership with INESC Porto is very present and has been important to the development of new production and maintenance solutions. Within the PRODUTECH (Production Technologies Competitive Pole of Portugal) initiative ADIRA participates in several RTD activities, including the field of intelligent production systems and predictive maintenance.

Role in the project:

ADIRA will have an important role in the project requirements analysis and specification, within Task 1.2. Will follow closely the project developments within WP 3, 4 and 5. Will implement the changes and interfaces necessary to integrate the MANTIS tools with ADIRA’s selected equipment. Will participate also in the activities related with Business impact within WP6. In WP7 ADIRA will participate in the implementation and validation of MANTIS in its equipment. ADIRA will participate also in the project dissemination and exploitation activities.

INESC TEC – Institute for Systems and Computer Engineering, Technology and Science

INESC TEC is a private non-profit research institution, with around 700 integrated researchers (about 350 PhD). Main activities are scientific research and technological development, technology transfer, consulting and advanced training programs in: Industrial and Manufacturing Engineering, Business Networking, Information Technologies, Energy, Telecommunications and Electronics, and Innovation Management. The Centre for Enterprise Systems Engineering (CESE) is composed of about 60 researchers (16 PhD) and includes activity areas related with Operations Management and Enterprise Information Systems, applied to industrial companies and enterprise cooperation networks. The main RTD areas are: Enterprise Cooperation Networks; Operations Management; Industrial Business Analytics; Advanced Planning Systems; Intelligent Logistic Systems and Interoperability. The Centre promotes applied research projects, in partnership with software houses and equipment suppliers, aiming at the development of innovative products in: decision support systems, production planning, scheduling and control, advanced automation and logistics, quality and maintenance management, knowledge management, and integration infrastructures. INESC TEC is a network of research centres, recognised as Associated Laboratory by the science ministry. INESC TEC has a strong link with industry, resulting from a long collaboration in RTD and consulting projects. It is present in the board of the Manufuture European Technology Platform and EFFRA Research Association.

Relevant Expertise for the project:

INESC TEC brings to the project specific expertise in the design and development of decision support systems, business analytics, data mining and forecasting and its application in the predictive maintenance domain. INESC TEC has experience in the design and development of decision support systems, business intelligence and analytics systems as well as data mining and machine learning technology to various problems. In particular, some of the problems which are particularly relevant for the current project are in manufacturing, including production and equipment monitoring and optimisation (modelling equipment functioning for operation parameterisation and machine failure prediction) and tool monitoring and design (modelling tool wear and selection). Other application domains include retail (recommender systems, sales prediction, shelf space allocation), logistics (forecasting), ebusiness (recommender systems) and structural health monitoring (failure identification). In order to solve such a diverse set of problems, INESC TEC can tap into a large network of researchers that have expertise on a set of diversified sub-fields of the area. This means that many different techniques in machine learning and data mining have been employed and their choice depends specifically on the type of problem to be solved. The knowledge about the techniques is complemented with extensive experience in their application to real-world problems and the corresponding know-how in all the steps of the process (including business and data understanding, data preparation and model deployment). This knowledge is complemented with the ability to conduct academic research to develop new approaches when the existing ones are not suitable. This includes, among others, anomaly detection, optimal model selection, meta-learning, transfer learning, learning algorithms specifically designed for streaming data and big data analysis (parallel and distributed data processing).

Role in the project:

Within WP 4 – Analysis and decision making functionalities INESC TEC will participate in the design and development of specific data analysis and forecasting tools especially using techniques such as Data Mining e Machine learning and algorithms to select the best forecasting models for each operating condition. INESC TEC will coordinate the use case implementation at ADIRA with contributions in Tasks 1.2 and WP7. Will also participate in the dissemination and exploitation activities within WP8.

Instituto Superior de Engenharia do Porto (ISEP)

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ISEP is the Engineering School of IPP (Polytechnic Institute of Porto), the largest IPP School, ISEP detains relevant human and material resources in research and development (R&D), particularly with regard to applied research in Engineering. ISEP’s participation in MANTIS is lead by CISTER research unit and counts with the participation of GECAD research unit. CISTER is one of the leading international research centres in real-time and embedded computing systems. In both the 2003 and 2007 evaluation processes, the Unit was granted the classification of ‘Excellent’ (the highest possible mark at that time). CISTER has been focusing its activity in the analysis, design and implementation of Real-Time and Embedded computing Systems. Particularly, CISTER has provided advances in architectures for distributed embedded real-time systems, real-time wireless sensor networks, cyber-physical systems, middle-wares for embedded systems and on the usage of multicore processors on real-time systems. CISTER is consistently involved in several collaborative EU and national RTD projects and initiatives. Relevant to this project, the unit was a core partner in the ArtistDesign and CONET (Cooperating Objects) European NoE, EMMON (EMbeddedMONitoring) projects, lead the international SENODs project (together with Portugal Telecom and the Carnegie Mellon University), on energy-e efficient data centres. Currently, CISTER is working on the ENCOURAGE (Embedded iNtelligent COntrols for bUildings with Renewable generAtion and storage) and Arrowhead projects, which target smart-grids and cooperative automation on multiples fields (e.g. industrial, smart-grids). GECAD (Knowledge Engineering and Decision Support Research Center) focus its research in intelligent systems, decision support systems and knowledge based systems. In these areas GECAD is recognised as a very relevant international player, achieving remarkable scores in top-level scientific journals. Over the years GECAD has been developing relevant work and tools in the area of intelligent manufacturing, considering both planning and scheduling, by applying different techniques and algorithms e.g. machine learning, optimisation, modelling, simulation, intelligent interaction, ontologies and knowledge representation. GECAD has a very relevant participation in international projects, being coordinator of two FP7-MC-IRSES: ELECON (Electricity Consumption Analysis to Promote Energy Efficiency Considering Demand Response and Non-technical Losses) and EKRUCAmI (Europe-Korea Research on Ubiquitous Computing and Ambient Intelligence) and assuming responsibilities from technical coordination to WPs leadership in 5 Eureka projects.

Relevant Expertise for the project:

CISTER has a strong and solid international reputation, built upon a robust track record of publications, a continuous presence on program and organising committees of international top conferences and on a strategic mix set of national/international and scientific/industry driven projects. CISTER is currently among the international leaders worldwide in the area of Real-Time and Embedded Systems. In the last years CISTER has been focusing its effort on the design of WSN protocols and on WSN data processing strategies with a strong focus on timeliness guarantees. CISTER has also been deeply involved on the design middleware systems for data collection from WSN and Home Area Networks. GECAD expertise areas of intelligent decision making, forecasting, knowledge discovery and machine learning are completely aligned with several MANTIS tasks. During the last years GECAD has been applying these techniques in several national and international projects, as well as achieved a notorious score in scientific publications on these areas. Machine learning and optimisation techniques have been applied to a huge variety of domains, ranging from resources management to scheduling problems and to strategic behaviour in competitive environments. GECAD work on Knowledge discovery is related to all steps of the process, with special emphasis on Data Mining, particularly clustering and classification. Besides applying these techniques to data from several domains, our work is also contributing to improve algorithms and define innovative solutions that combine several algorithms. Intelligent and collaborative decision-making has also been applied to several projects, regarding ambient assisted living; tourism and manufacturing systems.

Role in the project:

CISTER will mostly focus its scientific and technical contribution on WP1,2, 3 and 7. In parallel CISTER will also be the leader of WP8 on Dissemination and Project Exploitation. In detail CISTER will contribute to the task 1.2, by providing inputs from the Portuguese pilot and with its knowledge in the industrial area. For task 2.1 CISTER will focus its attention on the middleware architecture, partially based on the results from SENODS, ENCOURAGE and Arrowhead projects. On WP2, CISTER will also contribute to the definition of the MANTIS interfaces and protocols, on task 2.4. On WP3 CISTER will contribute to the design of smart sensors (task 3.2) and on bandwidth use and optimisation (task 3.3). The main objective is to apply the research which has been performed at CISTER on WSN data processing strategies. GECAD contributions will be on WP 2, 4, 5 and 7. Regarding WP2 contributions will be on the information processing and decision-making (task 2.2). Particular emphasis will be on WP5 on task 5.1 and 5.3. in WP4 the focus will be on tasks 4.5 and 4.6 on optimisation techniques and collaborative decision making. In addition, GECAD will also participate on dissemination and exploitation within the WP8.

UNINOVA – Instituto de Desenvolvimento de Novas Tecnologias (UNINOVA)

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UNINOVA is a multidisciplinary, independent, non-profit research institute located in the metropolitan area of Lisbon, Portugal. UNINOVA has managed and participated in many national (FCT, QREN, etc.) and international research programmes (FP4, FP5, FP6, FP7, IMS, etc.) during the last 15+ years. UNINOVA pursues excellence in scientific research, technical development and advanced training & education. UNINOVA operates close to industry for ensuring proper technology transfer of results and alignment of RTD work to industrial needs. UNINOVA has strong links to academia for creating continuous knowledge loops in between research and education for advanced technologies. UNINOVA hosts various renowned centres of excellence that expose a wide variety of competences. In particular, and for this project, personnel from the Centre of Technology and Systems (CTS) will be mobilised. The UNINOVACTS is part of the national science centres network being qualified has “very good” by the Portuguese Science & Technology Foundation. UNINOVA-CTS researchers exhibit recognised RTD skills and competences in advanced integrability and interoperability concepts, methods and technologies, in view of high dimensional, highly distributed and heterogeneous, networked environments. They participate(d) in key national and international research projects, e.g.: FP5-IST-37368 IDEAS, FP6-IST-507849 ATHENAIP, FP6-IST-508011 INTEROP-NoE, FP7-ICT-213031 iSurf, FP7-288315 PROBE-IT, ARTEMIS-100261 SIMPLE, ARTEMIS/FCT-332987. They contribute(d) to the development of novel interoperability and integration approaches and its supporting solutions, e.g. Model Driven Interoperability (MDI), ATHENA Interoperability Framework (AIF), Plug’n’Interoperate (Plug’n’Play Interoperability), Multilayer MiddleWare Architecture for IoT (Internet-of-Things), amongst others.

Relevant Expertise for the project:

The UNINOVA-CTS research team commit to work in the project is especially skilled in systems’ integrability and interoperability. Researchers at UNINOVA-CTS exhibit strong competences in integration methods for complex systems and their solutions (service-oriented platforms, middleware technologies, etc.) and on data interoperability approaches for highly heterogeneous settings (plug’n’play interoperability, etc.). As such, UNINOVA-CTS researchers are well-suited for the advanced RTD work envisioned in this project on the realisation of a Factory level Data-oriented Service Bus for data distribution/delivery in industrial settings. Moreover, UNINOVA-CTS researchers are involved in the FP7-ICT-612329ProaSense ‘The Proactive Sensing Enterprise’ dealing with aspects of Proactive sensing especially applied to industrial settings (including maintenance applications). As such, UNINOVA is already actively involved at an EUROPEAN level at research and technological development on areas closely related to those of this project and performing advanced work on its domains of competence. At a NATIONAL level, UNINOVA-CTS is well integrated and provides complementary skills for the so-called “Portuguese” consortium that is promoting an industry use-case on sheet metal working machinery.

Role in the project:

UNINOVA will mainly focus its research and development work under WP2 for the realisation of a Factory-level Data Delivery Service Network, established based on a Self-Organised Data Distribution MiddleWare (DDMW) and that will provide data integration (including interoperability) and (distributed) storage services. This will make available vast quantities of factory-level data for applications to exploit. As such, UNINOVA will also be contributing to the in service platform architecture definition to be done under WP1. Additionally, UNINOVA will have some involvement in the context of WP3 on supporting the developments/integration of data sensing feeds into the data delivery service network, in WP4 for supporting the creating of the service interfaces for analysis and decision making functions, in WP7 related to the integration/validation work of the “Portuguese” use-case, and in WP8 on doing project dissemination (scientific papers, mainly) and research technology exploitation.

Rijksuniversiteit Groningen (RUG)

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The institute for Artificial Intelligence and Cognitive Engineering (ALICE) focuses on AI, machine learning, pattern recognition, cognitive modelling and multi-agent systems since 2001. The institute has about 40 researchers in these areas. The University of Groningen (Rijksuniversiteit Groningen) has over 5000 researchers and 24000 students. It is a top-100 ranked university in several global rankings. The research group that will be involved in the Mantis project is the APS group: Autonomous Perceptive Systems.

Relevant Expertise for the project:

The APS group of ALICE has a unique expertise in machine learning, pattern recognition and big data mining. Apart from innovations at the algorithmic level of reinforcement learning and support-vector machines, we have the world’s first 24/7 learning system, that involves elicitation of user ’labels’ for raw data over internet and a continuous, autonomous learning process. The amount of data runs in the hundreds of terabytes, the number of instances runs in the hundreds of millions of data objects, many of them described with high (5000) or extremely high (50000) dimensions (data columns). Our unique configuration is able to exploit the strong points of both the human and machine components of a learning system. The human input consists of labelling detected unknown patterns, this effort must be minimised. The competence of the machine is the ability to create a usable agglomeration of patterns from a massive data collection. Good algorithms will be able to surf on Moore’s law, i.e., their performance will improve as computers become faster. An example application of this approach is our Monk search engine for historical handwritten manuscript collections. In May 2014, this system was able to learn to read historical Chinese texts within two weeks, with limited human efforts. We firmly believe in the use of optimising closed loops (neocybernetics) in order to obtain effective, resilient and autonomous systems.

Role in the project:

We will focus on a real-life maintenance problem involving vision and other real-life sensing, where the problem consists of reducing MTBF for incidents which in the current practice can only be solved by human operator intervention. Using data collection and user-based (’ipad’-based) annotation of problem conditions during the industrial process, a continuous learning cycle is initiated. In the first stage of the project we will focus on forecasting and detection of the problem, using a basic signalling method. The sensing component ideally focuses on not one but on several possible problem classes at a sensor location. In the second stage of the project, we will try to actually close the loop by means of intervention using a modern flexible and cost effective robot systems for solving general problems, when necessary. Actuators may consist of simple pressured air or magnetic methods for, e.g., obstacle removal. The problem-solving system will gradually develop a repertoire of problem conditions and a probability distribution for corrective actions and their computed applicability (utility values) given the current context. The proposed solution entails a loose coupling between the production system itself and the problem solver but a tight description of problem conditions and corrective actions. We will provide solutions that allow for convenient reconfiguration and reuse of learned material. Our direct partner is Philips, who will provide the (warehouse) process data. RUG will implement the pattern matching method and learning cycle. We will make additional computing resources and storage available on the massive Groningen high-performance computing cluster

Technische Universiteit Eindhoven (TU/e)

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Eindhoven University of Technology (TU/e) is a research university specialising in engineering science & technology. Our education, research and knowledge valorisation contribute to:

  • Science for society: solving the major societal issues and boosting prosperity and welfare by focusing on the Strategic Areas of Energy, Health and Smart Mobility.
  • Science for industry: the development of technological innovation in cooperation with industry.
  • science for science: progress in engineering sciences through excellence in key research cores and innovation in education.

Excellent Education: We see it as our duty to train engineers to possess a sound scientific basis and scientific depth. They also have the necessary skills to successfully flourish in social sectors and functions. Bachelor College and Graduate School. With a view to the future, TU/e has begun a major educational reform. The Bachelor’s and Master’s programs remain but are being incorporated in a Bachelor College and Graduate School respectively. Students will be given more freedom and can even choose to follow a broadbased program with society-oriented subjects or opt for a very specialised science program.

Advanced quality research: With advanced quality research, the university contributes to the progress of technical sciences and thus the development of technological innovations. We focus on areas in which we participate in the international scientific community. This can play an important role. The TU/e wants to give significant impetus to the knowledge-intensive industries and other social sectors with a high or rapidly evolving technology-intensity.

Knowledge Valorisation: The TU/e puts emphasis on knowledge valorisation: research results are translated into successful innovations and serve as a basis for creating new products, processes and enterprises. We encourage students and staff to opt for entrepreneurship. Mission TU/e: With this profile, the TU/e profiles itself as a leading, international, in engineering science & technology specialised university. We offer excellent teaching and research and thereby contribute to the advancement of technical sciences and research to the developing of technological innovations and the growth of wealth and prosperity both in its own region (technology & innovation hotspot Eindhoven) and beyond. In short, the TU/e profiles itself as the university where innovation starts.

Relevant Expertise for the project:

TU/e has leading competences in sensor systems, net- worked embedded systems, data and process analysis, deep learning, all of which will be of key im- portance to MANTIS. Recently, TU/e created DSC/e, the Data Science Center Eindhoven, building on its leading position in the Data Science domain.

Role in the project:

TU/e will contribute to defining the use cases in healthcare and factory automation, extracting the relevant requirelments for the Mantis platform, and designing the platform architecture for preventive maintenance taking real-time constraints into account. TU/e will also explore and develop machine learning methods and tools for deriving failure predictions from the operational log data provided by the industrial partners. The failure predictions will be used for investigating various Page 224 of 279 ECSEL-2014-1 Full Project Proposal strategies for maintenance optimisation, taking into account the tradeoffs inherent in failure predictions due to the artefacts in real log data. Key personnel Dr. Johan Lukkien [M] chairs th

Science and Technology B.V. (S&T)

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Science and Technology BV (SNT) is a SME developing cutting edge technology for complex systems, including: (i) Scientific and Technical SW for signal processing, visualisation, analysis, data fusion, and quality control in the domains of remote sensing and global positioning systems. (ii) System Health Management SW for Prognostic Health Management (PHM) and Fault Detection Isolation and Recovery (FDIR) solutions for the aerospace, scientific systems, and industrial systems. (iii) Scientific Systems Engineering for complex engineering challenges by unique capability to bridge the scientific possibilities and the technical feasibilities.

Relevant Expertise for the project:

SNT’s available expertise includes the ability for high-performance data analysis for complex systems and advanced sensor systems. In its 15 years of existence, the company has managed and participated in several large projects on data analysis and system health management including: Earth Observation (EO) (such ENVISAT, GOCE, GOME, and many more), Galileo global positioning system (Signal In Space analysis), scientific instruments, industrial applications (harbour cranes, machines for the chip industry (ASML)), etc. SNT’s workforce comprises circa 80 highly educated people (MSc or higher). SNT has access to a large variety of modelling and analysis tools for complex systems including its in-house-developed system health management tool (Uptime) and sensor analysis tools. The experience in sensor system development and sensor data interpretation makes them ideally suited for the development of maintenance systems.

Role in the project:

  • Analysis of technical requirements for predictive management system.
  • Business case development for predictive management system.
  • Development of predictive management system.
  • Application of predictive management technologies to an example use-case