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Real-time Data-Driven Maintenance Logistics - Innovation in Data-Driven Maintenance Logistics
As part of the C2D project Real-time Data-driven Maintenance Logistics, researchers and companies collaborate to optimize maintenance processes through real-time data. In an interview with Willem van Jaarsveld (TU/e), one of the lead researchers, the main challenges and successes were discussed in the fall of 2024.
CIMPLO - Cross-Industry Predictive Maintenance Optimization Platform
Almost every enterprise has to deal with it: maintenance of machines and infrastructure. Traditional maintenance concepts rely on a 'fixed interval approach', taking into account a considerable safety margin. As a result, maintenance almost always occurs either too early or, in worse cases, too late. This arguably makes it one of the most inefficient and simultaneously critical activities in the industry. The CIMPLO platform aims to optimize the perfect moment (both cost and safety-wise) to perform maintenance on engines.
Smart Connected Bikes: Enhancing Cycling Experience with Technology
Cycling is popular in the Netherlands and continues to grow in popularity. In recent years, record numbers of e-bikes have been sold in the country. While the increasing popularity of e-bikes has many advantages, the number of traffic accidents involving cyclists has steadily grown over the past decade.
CERTIF-AI: Certification of the quality of the production process through artificial intelligence
A consortium of academic and industrial partners, led by Jheronimus Academy of Data Science (JADS) and Technische Universiteit Eindhoven (TU/e), is collaborating on the development of an Artificial Intelligence (AI) toolkit, consisting of algorithms and methods, for industrial applications. Production processes can be made 'smarter' by leveraging the data streams generated by the machines used in production. In particular, these data streams can be mined to build a model of the production process as it was actually executed - as opposed to how it was intended.
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