Diagnosing for printer maintenance with AI
When exactly does a building or item of equipment require maintenance? Carrying out too much preventive maintenance can be costly. But if you leave it too long, the expense of an emergency repair can be much greater. And we all know that machines have a habit of breaking down at exactly the wrong time. So predictive maintenance is high on the list of priorities of the manufacturing industry. Here, too, artificial intelligence (AI) can make all the difference.
A customer is itching to receive the printed matter he has ordered. The printers are running flat out – as are all the other machines. It is not just tough quality requirements that the graphic industry has to meet, but in many cases tight deadlines as well.
That makes it an ideal sector for introducing predictive maintenance based on artificial intelligence. And that is exactly what TNO and Canon Production Printing (formerly Océ) are currently aiming to do. Together, they are carrying out research into an AI system that stands out in terms of reliable diagnoses and prognoses for professional printers.
AI that understands printers
A lot of different data is needed to be able to estimate the condition of a machine or machine components. But much of that data is incomplete or unreliable. To be able to extract the right conclusions from this mix of data, you need AI that is entirely at home in the world of printers.
This calls for 'hybrid AI' – a combination of data-learning AI and domain knowledge. The AI system is able to modify the likelihood of causal links in the domain model on the basis of a machine’s user data. It can also work out what problems could occur and in what kind of time frame.
Still essential: human expertise
And what about people? The part they play should not be underestimated. Machine learning, then, is just one part of the story. It is precisely the combination of artificial intelligence and human expertise that make this solution so powerful. The big challenge here is to develop an AI system that is capable of accurately combining the input from people and machines alike.
Christopher BrewsterFunctie:Senior scientist
Christopher Brewster is a Senior Scientist in the Data Science group and Professor of the Application of Emerging Tecnologies in the Institute of Data Science, Maastricht University. His research has focussed on the application of Semantic Technologies, Open and Linked Data, interoperability architectures and Data Governance, mostly to the food and agriculture domains.
Daniël WormFunctie:Senior consultant
Joris SijsFunction not known
Judith DijkFunctie:senior research scientist
Judith is specialised in extracting information from camera images. She now applies the subject of her PhD thesis in Physics, which she obtained 18 years ago, to her work as a research scientist at TNO, including in a research programme on camera systems for the Dutch Ministry of Defence.
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You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?
Innovation with AI
What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.
Innovating with innovation AI
How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?
David Deutsch on the development and application of AI
Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?
Georgette Fijneman on the promise of AI for health insurers
Hanneke Molema, senior consultant healthy living at TNO, interviews Georgette Fijneman, CEO of health insurer Zilveren Kruis since 2017. Both look at the same topic, health, from a completely different perspective. What is the promise of AI for one of the Netherlands’ largest health insurers?