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

Philips Electronics Nederland B.V. (PHILIPS)

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Royal Philips is a diversified health and well-being company, focused on improving people’s lives through meaningful innovation in the areas of Healthcare, Consumer Lifestyle and Lighting. Headquartered in the Netherlands, Philips posted 2013 sales of EUR 23.3 billion and employs approximately 115,000 employees with sales and services in more than 100 countries. Philips Research in Eindhoven, which is part of the Philips Group Innovation (and its legal entity Philips Electronics Nederland), employs approximately 1000 researchers. Within Philips research work is carried in three programs aligned with Philips businesses: healthcare, consumer lifestyle and lighting. Data driven research and service orientation is common for all three programs. Data analytics plays there an important role. Therefore Philips research is involved in many research projects in this domain, both internal for Philips businesses as external. Philips Research has a long heritage of pioneering innovation and applying this to specific application areas such as healthcare. Philips Research is very active in partnering with universities and currently has more than 50 running FP7 projects and flagship programs with different universities (e.g. more than 70 PhD students with Technical University of Eindhoven). The Data Science Department of Philips conducts data analytics research for Philips businesses and it is involved in several EU projects such as AU2EU, TClouds, ATTPS etc.

Relevant Expertise for the project:

Data Science department consists of research and senior scientist with competences in machine learning, data mining, statistics, probability theory, advanced data management and computing as well as in other data science sub-fields. The department is involved in many internal projects where data analytics is applied to bring value to Philips.

Role in the project:

PHILIPS will contribute mainly to WP 4 working on the following specific tasks: (i) providing data flow for real-time processing and analysis, (ii) modeling and integration aspects, (iii) developing methods for predictive maintenance by combining expert knowledge and machine learning algorithms covering also privacy and data uncertainty aspects; (iv) developing algorithms to predicted remaining lifetime of the parts of imaging systems and (v) developing metrics that can be used to make a balanced trade-off between the cost of a failure and the cost of the predictive maintenance. Next to that PHILISP will provide contributions to dissemination and exploitation activities.

Philips Medical Systems Nederland B.V. (PHC)

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Royal Philips Electronics is a main electronics company focusing on healthcare and well-being. In healthcare, Philips’ innovation revolves around improving the quality and efficiency of healthcare through a focus on care cycles. Central to care cycle thinking is a patient-centric approach that optimises healthcare delivery for all the major diseases. In the Philips Healthcare (PH) sector, over 12% of systems sales are invested in R&D. Philips combines its expertise in medical technology with clinical know-how of its customers to produce innovative solutions that meet not just the needs of individual patients, but which also enable healthcare professionals to work faster, more easily and more cost-effectively. While PH has a large global organisation, in the Netherlands more than 3000 people work at PH, of which 1000 in R&D. Sales of PH’s total sector amounted to e 9,5 B in 2013. Philips is globally number one in medical diagnostic imaging and patient monitoring. PH participates in the Mantis project with Business Group Imaging Systems (IS), which is responsible for the imaging equipment, i.e., MRI, CT, X-Ray, Nuclear Medicine, Ultrasound. The IS Customer Services department of Imaging Systems is involved, working on the implementation of an overall service strategy. In the Mantis project PH will focus on the interventional X-ray equipment and MR equipment. The Business Innovation Units MR and interventional X-Ray will participate with their customer services and R&D departments. At PH in Best the BIU Interventional X-Ray is responsible for marketing, service, development and manufacturing of interventional X-ray systems used in the area of cardiac or vascular medical diagnosis and intervention (e.g. “dotter” treatment, orthopedic surgery). Its customers are hospitals and university medical centers. Research and innovation of the BIU focus on software, (digital) electronics and mechatronics. The other PH department in the project develops complete MRI scanners. It focuses on the development of magnets, coils, hardware, mechanics and software for data acquisition, serviceability, patient administration and image viewing. Development is done in close cooperation with clinical scientists, applic- ation specialists as well as research institutions inside and outside PH, including many renowned hospitals. PH is a leading innovator in the area of reliable MR imaging. It’s MR multi-channel functionality improves image quality and accelerates scanning of brains and other anatomies, reducing scan times for patients and making life easier for the MR operator. Specific examples are the introduction of parallel imaging (SENSE) to speed up imaging and reduce image artefacts and the introduction of Multi-Transmit to reduce spatial variation. More information on PH can be found at

Relevant Expertise and Role in the project:

  • Philips allows the research partners to use the log data (and specific work order data) for this research.
  • Philips indicates to the research partner the business-relevant diagnostic topics
  • The research partners performs their research aiming to come up with relevant diagnostic rules/predictors.
  • If the research partner is successful, Philip can use the results of the work
  • In several Mantis deliverables, the research partner and Philips cooperate on the same deliverable. Typically either one of them has the lead, and the other contributes.
  • We will have to link the work of the research partner to either iXR or MRI data, or both.
  • Philips will develop a framework for applying the research on industrial use cases
  • Philips and the research partners will provide prototypes that validate the result of the research on industrial use cases

Philips Consumer Lifestyle B.V. (PCL)

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Our Consumer Lifestyle brand plays a central role to fulfil the needs of consumers worldwide. In this way, we inspire them and make it possible to lead a meaningful life, stay healthy and enjoy life. Consumers worldwide want to improve their own health and wellbeing as well as that of their relatives and families. Philips Consumer Lifestyle wants to grow to become a key player in the area of health and wellbeing by constantly delivering relevant and meaningful innovations. Our key strategic advantage is a combination of our global strong brand, our insights in wishes and needs of people, our extraordinary expertise on technology and design combined with the numerous collaborations with our distribution channels, partners and supply chain. Production is not a goal on its own. Philips delivers intense experiences matching social and emotional needs of our clients in their home situation. We deliver custom solutions aiming at global differences, from a cup of coffee to start the day to a healthy evening meal. Whether we are talking about our sonic technology for best oral care or our innovative laser guided beard trimmer, the innovations that Philips Consumer lifestyle delivers are important for our clients and improve their lives, every day.

Relevant Expertise for the project:

Philips Consumer Lifestyle is world leader in mass production of rotary shaving, occupying over 50% share of a 1.1 billion market. For more than 60 years we have been manufacturing shaving systems in our factory in Drachten. Here we have a highly automated production environment generating large amounts of data. We are the only shaving head manufacturer that applies the ECM process to shaving systems. The characteristics of this process gives us an advantage in shaving performance. However, low cost alternative production processes of the non EU competition are being refined each year, making it essential for PCL to improve on quality, product performance and costs to be able to withstand competition from non EU competitors. During the Mantis project, PCL will make a large amount of production and maintenance data available for development and validation of the Mantis project. The results of this project will reveal critical process parameters relating to maintenance. We have several production lines ready for the results to be implemented on. Ultimately, we gain insights in effective maintenance protocols, better product quality control, including less waste and downtime. This knowledge can be used to be applied to other production processes operated in EU production facilities.

Role in the project:

  • Philips allows the research partners to use production data for this research
  • Philips indicates to the research partner the business-relevant maintenance topics
  • The research partners performs their research aiming to come up with relevant diagnostic rules/predictors.
  • If the research partner is successful, Philips can use the results of the work
  • In several Mantis deliverables, the research partner and Philips cooperate on the same deliverable. Typically either one of them has the lead, and the other contributes.
  • Philips and the research partners will provide prototypes or pilot lines to validate the result of the research on the shaver plant industrial use case.

3E n.v. (3E)

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Established in 1999, 3E is an independent technology and consultancy company. 3E provides solutions as well as guidance to improve renewable energy system performance, to optimise energy consumption and facilitate grid and power market interaction. 3E pursues innovation to provide leading energy intelligence and practical solutions to our customers. 3E has worked on projects in more than 30 countries and operates with an international team of around 80 experts from its headquarters in Brussels and offices in Toulouse, Beijing, Istanbul, Cape Town and London. 3E is certified ISO 9001:2008 since early 2010.

Role in the Project and Relevant Expertise:

Contribution to WP1: 3E will contribute with a study on the state-of-the-art of proactive maintenance of photovoltaic plants, with user scenario requirements providing input and use cases of the MANTIS project based on customer feedback and internal experience gained through building and developing the SynaptiQ Photovoltaics monitoring platform, and by providing its architecture and technology knowledge used in the SynaptiQ platform keeping it in line with the constantly evolving technologies.

Contribution to WP2: 3E will contribute by providing its experience and knowledge in monitoring platform providing input in big data Database, Service oriented architecture, Infrastructure architecture but also provide operational input and guidelines which can influence architecture decision due to their consequences on maintenance and platform evolution / migration capabilities. 3E has developed strong knowledge in ETL and interoperability strategies and will contribute by providing architectural and design to ensure input data and service interoperability. The SynaptiQ team is using agile methodology with acceptance criteria to validate user stories implementation, its QA team has acquired strong knowledge in ensuring correct implementation and objective validation of features in the scope of continuous deployment, which is valuable for the use case definition (T1.2) but also in use case validation in order to ensure coherence in the project and correct implementation of features.

Contribution to WP3: 3E will contribute to the physical modelling of various measured data in photovoltaic plants. Two main types of models will be worked upon. The first type of model reflects the system characteristics as expected before production started, building upon state-of-theart theoretic models. The second type of model is a sort of update of the first (theoretic) model type, with its parameters corrected during operation to reflect the real characteristics of the PV system. In T3.4, 3E will contribute to the automated validation of irradiance, temperature and electrical data sensors. For example, the orientation, inclination, time synchronisation and calibration of irradiance sensors could be validated by use of nonlinear regression techniques. The models developed in T3.1 are a useful reference for this validation.

Contribution to WP4: 3E will particularly contribute to the development of functions for root cause failure analysis in the field of electricity production and, in particular, electricity production by photovoltaic plants. In this field 3E will also contribute to the development of methods for alerting and prediction of asset failures in view of maintenance optimisation for these plants. 3E’s team for software developments has a track record of 5 years in the development of monitoring software including functions for alerting and maintenance support. 3E’s R&D team has been exploring and successfully applying mainly regression methods for data analysis from photovoltaic plants for more than three years. These methods have gradually been implemented in our production environment. Consequently, 3E’s contribution to this work package will start from a high level and will always be linked to the practical applicability of the solutions explored. In order to come at a real pro-active maintenance, 3E will contribute by developing methodologies to optimise both selection and scheduling of maintenance activities, ensuring early detection and mitigation of degradation issues and component failures, while simultaneously reducing required maintenance efforts.

Contribution to WP5: 3E will contribute with mock-up, interface modelling and requirement specification experience of the SynaptiQ team, and by providing an upfront analysis of regular tasks and user journeys for each stakeholders, in order to provide useful information on how, when and what to present to each user, minimizing time spent, complexity, thus improving efficiency. Last but not least, efficient interface goes though reactivity and quick response time, intelligent backend analysis in order to support the frontend interface. 3E will therefore provide its experience on all these topics in order to contribute to this work package.

Contribution to WP7: 3E is in charge of demonstrating solutions for asset management in energy production. In particular, 3E will implement them for the use case of photovoltaic plants. 3E can dispose over monitoring data from 1800 commercial-scale photovoltaic plants, of which 3E will identify a sub-selection serving as a portfolio for the demonstration. First, 3E will analyse the historical data from these plants based on the analysis and decision making functionalities developed for this use case in WP4. Subsequently, the analysis and decision making functions, as well as the HMI interface options developed in the MANTIS framework WP 4 and 5, respectively, will be tested and applied to the real-time monitoring data feed from the photovoltaic plant portfolio.

Contribution to WP8: 3E will contribute to the dissemination of lessons learnt: Proposals for better system design / configuration / monitoring practices in order to reduce production losses as a result of degradation will be disseminated at relevant conferences, workshops and/or seminars.

Contribution to WP9: 3E has experienced staff for scientific and organisational project management with a strong track record in project management and will ensure professional contribution to the management tasks from their side

Atlas Copco Airpower n.v. (ATLAS)

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Atlas Copco is a world-leading provider of sustainable productivity solutions. The Group serves customers with innovative compressors, vacuum solutions and air treatment systems, construction and mining equipment, power tools and assembly systems. Atlas Copco develops products and service focused on productivity, energy efficiency, safety and ergonomics. The company was founded in 1873, is based in Stockholm, Sweden, and has a global reach spanning more than 180 countries. In 2013, Atlas Copco had revenues of BSEK 84 (BEUR 9.7) and more than 40 000 employees. Atlas Copco is listed on the NASDAQ OMX Stockholm exchange. There is always a better way. For more than a century this guiding principle has driven Atlas Copco’s engineers and designers to develop new technology and revolutionise productivity in drilling, manufacturing and other industrial processes. Many of the Group’s products have become global success stories. Atlas Copco continuously searches for external partnerships to build long-term relations with inventors, customers, suppliers, academia, research companies and institutes. Collaboration is an important key element in our innovation process to meet customer demands. Atlas Copco has around 2 500 employees conducting research, design and development at dozens of sites around the world. The Group’s main R&D hubs are in Sweden, Belgium, India and China, but there are R&D operations in many other countries. Atlas Copco’s Airpower facility in Belgium is the world’s largest and most advanced manufacturing site for both portable and stationary compressors, employing around 2 600 people of which around 500 engineers are engaged in advanced product design and development as well as research on topics such as energy recovery, air filtration and separation and much more. Atlas Copco Airpower is the head office of Atlas Copco’s business area Compressor Technique, which had revenues in 2013 of MSEK 33 823.

Relevant Expertise for the project:

Atlas Copco is worldwide market leader for air compressors and has a strong focus on service. A substantial share of its revenues is generated by service (36% for the Compressor Technique business area in 2013 compared to 58% from equipment sales). Additional to product innovation, Atlas Copco invests substantial effort in service innovation. Atlas Copco already has a track record of more than 10 years in connecting compressors for remote monitoring. Today its SMARTiink solution connects already thousands of compressors and the ambition is to grow substantially in the future. We have extensive experience in connectivity, data mining, condition monitoring and business development related to maintenance services. In this project more than 10 employees with relevant expertise will be directly involved and others indirectly (amongst several Phd’s). We already have expertise in working on related topics with a.o. FMTC and KU Leuven.

Role in the project:

In the MANTIS project, Atlas Copco will develop of a ”health map” for connected compressors that supports decision making for maintenance services. The topics we will work on : • Service platform architecture : application of this on a large scale industrial network of connected compressors (WP1). • Smart sensing and data acquisition + analysis and decision making : using machine learning techniques for some of the intelligence ’under the hood’ of this health map (WP3 + WP4). • HMI design and development : visualisation of information for the ”health map” (WP5).

Ilias Solutions n.v. (ILIAS)

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ILIAS Solutions is a Brussels based SME. ILIAS Solutions is a SW development company, specialised in information management solutions to support defence and humanitarian aid operations. Our software platforms are centred on the optimisation of operational readiness, and based upon the management of the resources required for those operations.

Relevant Expertise for the project:

ILIAS Solutions consists of an effective team of technical experts in the development, implementation and maintenance of software systems, with experience in managing complex and sensitive assets maintenance and logistics systems. Our team possesses a wide variety of skills ranging from business analysis, process improvement and project management to software development, testing, training, maintenance and help desk services.

Role in the project:

Our main contributions will fall under WP4+WP5, where we will provide development and analysis competences, however we will need to keep ourselves involved/informed on especially the WP1+WP2. We could also be involved in WP8 and take the findings to MRO and Defence Logistics conferences as Key note speakers

Sirris c.d.g. (SIRRIS)

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Sirris is a private collective research centre founded in 1949 by Agoria, the multi-sector federation for the Belgian technology industry. Sirris has about 2500 member companies, which range from small and medium-sized businesses to multinational companies (¿95% SMEs), active in 13 technological sectors including Information & Communication Technology. Its mission is to improve the competitiveness of its member companies through technology and innovation. Sirris has currently about 140 employees focusing on knowledge acquisition (applied research) and knowledge transfer (industrial projects). The subdivision ICT & Mechatronics of Sirris will participate in MANTIS.

Relevant Expertise for the project:

Within Sirris ICT & Mechatronics, the expertise with relation to advanced data processing is bundled in the Data Innovation team, a small group (6 team members, all with PhD degree) of highly qualified experts who participate in industry-driven research projects in collaboration with national and international partners (e.g. 6 ARTEMIS projects). The team has set up several industry-driven research programs through which it has created a broad network of international research and industrial partners. Through these projects, the team has acquired extensive knowledge in various areas related to topics such as scalable data processing, complex event processing, pro-active and context-sensitive decision support, user profiling and modelling. Through participation in these projects, the team has created a broad network of international research and industrial partners, such as for example Fundaci´on Tekniker, ST Microelectronics, Tecnalia, ATOS, Tampere University of Technology, Philips, Thales, Institut Polytechnique de Bordeaux, Technical University of Sofia and TNO. On national level, the team has established very successful collaborations with several different academic organisations in Belgium e.g. iMinds, ULB, VUB, UGent, KULeuven, KAHO St. Lieven, etc.

Role in the project:

In MANTIS, Sirris will:

  • contribute to the definition of the service platform architecture (WP1) and to the development of several of its components (WP2), most notably:
    • distributed data storage components for storing large amounts of (historical) data obtained from heterogeneous sources
    • distributed stream-based data processing for supporting (near-)real-time anomaly detection
    • batch-oriented data processing for building predictive models based on trend and pattern detection in historical data.
  • explore the use of semantic technology for integration, harmonisation and standardisation of heterogeneous data, e.g. operational data, maintenance and event logs, external data sources such as ERP systems, etc. (WP4)
  • apply advanced data processing algorithms for knowledge discovery, e.g. temporal data mining algorithms (e.g. sequential pattern mining, temporal association rules, …) for discovering root cause failures and events that co-occur in a certain order, probabilistic modelling approaches (e.g. Bayesian networks) for discovering complex relationships between the different events and for predicting asset failure, multi-criteria decision analysis for collaborative decision making, etc. (WP4)
  • validate the developed approaches, methodologies and resulting models in specific use cases