Resources & News

​We are very proud to announce we are the winner of the Fluvius challenge to predict failures in electrical substations.
We ended up second (out of 13 contenders) in the Aquafin challenge to predict failures in pumping stations.
In both cases we collaborated with domain experts to combine the best of Artificial Intelligence and Human Expertise: in the Fluvius case with Engie-Laborelec and in the Aquafin case with i-Care.

If you haven't been able to follow the presentations during the conference, you can find the videos used in those presentations  here.

Learn more about other domains of application for machine and asset data analytics here.

Follow the link to read about use cases for:

• Equipment manufacturers
• Process industry and continuous production
• Batch production and discrete manufacturing
• Utilities and power generation
• Iot, monitoring and data collection platform vendors
• Third party equipment monitoring and service providers

We are very proud to announce we are the winner of the Fluvius challenge to predict failures in electrical substations. We ended up second (out of 13 contenders) in the Aquafin challenge to predict failures in pumping stations.
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In both cases we collaborated with domain experts to combine the best of Artificial Intelligence and Human Expertise: in the Fluvius case with Engie-Laborelec and in the Aquafin case with i-Care.

If you haven't been able to follow the presentations during the conference, you can already find our video demonstrations of Yanomaly as featured during the competition by following this link.

​TietoEVRY and Yazzoom partnership promotes the use of Artificial Intelligence in the pulp, paper and fibre industry.
Data analysis in mills has a long history, but new technologies are making the next level possible through AI and machine learning. Yazzoom provides a rich set of tools and algorithms, from simple single sensor anomaly detection all the way to real-time predictive modelling and optimisation. TietoEVRY will embed these capabilities in its TIPS industry solutions and services. ​Click for more.

Includes 3 customer stories using Yanomaly.

​Asset monitoring systems based on artificial intelligence allow industries to reduce unplanned downtime, reduce mean time-to-repair, and increase the return on investment of maintenance operations in both the manufacturing and process industries.
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In this project, 38 partners from six countries will be involved in research on the smart networking of development and production processes for electronic components and systems.
Development and production teams will be networked with each other, independent of their locations, and communicate in real time along the value chain. Processes can be virtually mapped by digital factory and product twins and can thus be simulated comprehensively.
Artificial intelligence and machine learning play a central role in all of this.

 Read More. 

An article from our customer Water-Link, on the Proof of Concept project on the detection of anomalies in the water network of Antwerp.

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Reflexion will optimise the full end to end product development lifecycle and maintenance process, bringing in analytics to automate and complement expert knowledge, and enabling predictive maintenance on a broader industrial scale and shortening product evolution development iterations.

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The demonstrator is based on a virtual industrial system (i.e., a production line with multiple machines), which has the common functionalities of high-tech systems, such as lithography machines and professional printers, etc. We have developed a simulator of the system and generate the operational data. As an industrial showcase, ESI’s demonstrator presents “Smart high-tech system-diagnostics with operational data”. 
​One of the fundamental issues of root-cause analysis is how to detect anomalies from data. Yazzoom’s advanced unsupervised anomaly detection techniques have been applied by ESI’s demonstrator in the following two aspects.

​To diagnose a specific machine issue and narrow down the space of causes, we use the operational data generated by the machine components. Basically, anomalies are identified based on the operational data. This is achieved using unsupervised learning techniques, which are able to detect anomalies without efforts from domain experts. 

Read more.