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.