React to Effects Fast by Learning, Evaluation, and eXtracted InformatiON
Reflexion project aims at supporting the emerging high-tech industry paradigm shift from selling ‘boxes’ to supporting ‘integrated solutions’, by providing significant improvements in quality and stability during early product roll-out.
- Reacting to unforeseen problems or emerging needs in a speedy and cost-effective way by augmenting products with an introspective layer of data sensing and data analytics.
- Thereby creating value out of the high-tech system’s operational data which for now is still characterized by legacy choices on infrastructural and analytical approaches.
- Propagating this knowledge (automatically) back into the product development lifecycle and the service & maintenance flow.
Vision and challenges
To attain / maintain (global) leading positions in today’s globalized competitive markets, the OEMs need to bring latest technology products to market, with highly efficient time-to-market, limited costs, and controlled quality level. Challenges and opportunities for the Reflexion project:
- Better understanding of customer needs, way of working and operational context will enable focus in R&D investment.
- Early market introduction leads to better competitive positioning.
Note: only top 3 players have success, however only true when the market introductions meet adequate quality criteria.
- Industrial costs of poor quality sometimes amount to up to 5% of total turnover, typically more than 20% of the R&D budget is spent on resolving poor quality related problems.
- There is a strong industrial need to employ scarce human resources to the R&D activities that generate the most business impact.
- Access to the OEMs development processes directly translate in new primary business capabilities, for example by creating opportunities to develop new tool functionality and services for an as of yet underexplored area of application.
- Possibility to incubate and validate proof of concepts for (general purpose) tool functionality and services in industrial context.
- Improve visibility towards the OEM industry advancing the tool and service providers market penetration.
ESI develops generic methodologies that integrate domain knowledge and data into a system-level reasoning framework.”. As shown in the above figure, for knowledge engineering, ESI uses domain-specific languages to systematically model domain knowledge, which is often scattered across documents, or in the heads of experts. For data-driven business applications, knowledge engineering supports the effective analysis of operational data, by applying, for example, feature selection or constraints in learning algorithms. Exploitation of profiling, process and data mining techniques allow the generation of context-specific operational models that can either support, among others, automated testing or customization of system operations or be used to develop new systems.
Reflexion project figures out the main challenges are how to valorize the emerging operational data in high-tech systems, how to react to emerging needs in an efficient and cost-effective way by augmenting products with an introspective layer of data sensing and data analytics? The learned lessons include
- Exploitation of high-tech system’s operational data is highly characterized by legacy engineering choices on infrastructural and analytical approaches.
- The high-tech industry (R&D) still has an immature view on the exploitation of data science for valorization of operational data. Building up awareness, that integration of data into smart engineering processes can become a valuable and competitive business asset, currently turns out to be more important than building up expertise.
- Focus should be bridging the world of data science with the world of high-tech systems development, by exploiting data analytics expertise / techniques to extract value out of existing operational system logging.
- High-tech systems are quite diverse: there are little to no out-of-the-box generic (data science) approaches, in reality it requires serious domain insight / modeling mixed with real system understanding craftmanship.
|TNO||System-data demonstrator: industrial showcases of how opertional data adds value to high-tech industry.||Bas Huijbrechts|
|SynerScope||First market releases of SynerScope's Ixiwa and Iximeer products suite: quickly navigate, search, link and improve structured and unstructured data; explore all dimensions of the data at once.||Jorik Blaas|
|Yazzoom||Yanomaly platform for anomaly detection on machine-generated data is enhanced to deal with more diversity in data.||David Verstraeten|
|Axini||Axini TestManager model-based testing tool suite now incorporates the usage models learnt from machine log data.||Machiel Van der Bijl|
|Barco||Developed and released Nexxis Care Plan, Barco's web portal for its distributors, resellers and system integrators to manage their installed Barco products.||Wim Sandra|
|Philips||Developed new Image Guided Therapy system verification processes using model-based testing incorperating usage models learnt from field log data by applying machine learning techniques.||Rob Ekkel|
|Philips||Developed and released a data analysis framework which collects & visualizes machine data of the fleet of operational MRI systems, supporting services incl. MRI system dashboards for commercial customers.||Mark van Helvoort|
|Océ||Developed the Optimal Diagnostic Analysis System (ODAS) data infrastructure which supports data analytics using Python Jupyter Notebooks for R&D purposes.||Lou Somers|
|Siemens ISW||Developed and validated a methodology to train machine learning algorithms with simulation-generated data for conditional monitoring of machines.||Bram Cornelis|
|all partners||25 data science specialists (FTEs) were employed by the consortium partners to work on operational data roadmap activities as a direct result of the project activities.||Bas Huijbrechts|
|Axini, SynerScope, Yazzoom||10 commercial tool set releases including - in the project developed - generic purpose data visualization, analytics and MBT functionality to be applied in an industrial setting directly resulted from the Reflexion project activities.||Bas Huijbrechts|
Probabilistic string clustering
|Eline Verwielen, Corrado Grappiolo, Nils Noorman, Kuno Huisman. "The Growing N-Gram Algorithm: A Novel Approach to String Clustering". Will be submitted to a conference. 2018||15-8-2018||TNO||Corrado Grappiolo|
|Probabilistic string clustering||Eline Verwielen. "Growing N-grams: a probabilistic approach to string clustering". Master thesis, Tilburg University, 2018.||8-3-2018||TNO||Corrado Grappiolo|
|Simulation-driven machine learning||Cameron Sobie and Carina Freitas and Mike Nicolai. "Simulation-driven machine learning: Bearing fault classification". Mechanical Systems and Signal Processing, pp 403-419, 2018||1-3-2017||Siemens Industry Software||Cameron Sobie|
|Fleet-based fault detection||Hendrickx, Kilian & Meert, Wannes & Cornelis, Bram & Janssens, Karl & Gryllias, Konstantinos & Davis, Jesse. "A fleet-wide approach for condition monitoring of similar machines using time-series clustering". International Conference on Condition Monitoring of Machinery in Non-stationary Operations (CMMNO2018), 2018.||20-6-2018||Siemens Industry Software||Bram Cornelis|
|The value of exploiting data||ITEA Magazine #29||16-3-2018||TNO||Bas Huijbrechts|
|Smart high-tech system-diagnostics with
|Yonghui Li, Marina Velikova, Emile van Gerwen, Martijn Hendriks, Michael Borth, Koen Kanters. "Demonstrator of high-tech system-diagnostics with operational data". ICT.OPEN 2018.||12-3-2018||TNO||Bas Huijbrechts|
|Data-science, een kwestie van goed samenwerken||Joost Janse. "Data-science, een kwestie van goed samenwerken". Bits&Chips, July 2016.||19-7-2016||Océ||Joost Janse|
|Data mining for condition monitoring||Sander Castermans, "Data mining techniques for machine/vehicle condition monitoring", MSc Thesis, KU Leuven, 2017.||28-6-2017||Siemens Industry Software||Bram Cornelis|
|Bearing fault Detection||Paulo Sousa, "Bearing fault detection: a feature-based approach", MSc Thesis, Univ. Porto (FEUP), 2018.||29-1-2018||Siemens Industry Software||Bram Cornelis|
|Semantic Search Engine||Corrado Grappiolo, Emile van Gerwen, Jack Verhoosel, Lou Somers, “The Semantic Snake Charmer Search Engine, a Tool to Facilitate Data Science in High-tech Industry Domains", in Proceedings of the ACM SIGIR Conference on Human Information Interaction and Retrieval, March 2019 (demonstration paper, under review).||12-11-2018||TNO, Océ||Corrado Grappiolo|
Kambiz Sekandar. "A quality measure for automatic ontology evaluation and improvement". Master thesis, Utrecht University, 2018.
Reflexion project period
September 2015 - February 2019
The ITEA3 14035 REFLEXION project is
supported by the Netherlands Enterprise
Agency (RVO) and Flemish Agency for
Innovation and Entrepreneurship (VLAIO).
+31 88 866 54 20 (secretariat)
“My drive is to scale up knowledge valorisation, balancing research objectives against the industrial expectations, for architecting and designing complex systems into the high-tech embedded industry for real industry valorisation.”