predictive maintenance

Predictive maintenance now possible thanks to Artificial Intelligence

Whilst Artificial Intelligence has for a long time been likened to something out of science fiction, it has now started making its way into our daily lives. Today, tech giants are focusing all their attention on improving their algorithms, which will go on to become a major cross-sector asset. 

Artificial intelligence in 2017

If there’s one thing that multinationals are fixated on in 2017, it’s Artificial Intelligence. IBM, for instance, created Watson, a purpose-built centre in downtown Munich for developing its AI. The centre is being used as a test framework for experimenting the use of AI within businesses. Moreover, Apple recently launched a blog targeting both developers and the general public to demonstrate its advances in Machine Learning. In addition, Microsoft will incorporate AI into the next generation of its HoloLens devices and will continue the development of its Cloud Azure infrastructure.  

At the most recent Google I/O conference, Alphabet launched its SaaS Machine Learning project via Google Cloud. The tech giant also presented the progress made in Assistant and its image recognition system, which now makes fewer errors than, well, humans. The company isn’t stopping there, however. Alphabet is now launching google.ai, a site that will demonstrate its vision of “bringing the benefits of AI to everyone.” At the same time, some Facebook chatbots have successfully developed their own unique way of communicating together.

Linking all these different projects together is the same, common goal: to eliminate repetitive, expendable tasks, and to help humans and their companies accelerate their advances in tech.

Ending machine breakdowns with predictive maintenance

A production line breakdown in industry is tantamount to a dead loss in profits for business. Today, combining Big Data and AI means that companies can perform predictive maintenance, and therefore, be more efficient.

Each breakdown sends a weak signal and is the result of a build up of other weak signals on the machines. Without these tech tools, it is simply impossible to predict breakdowns before they happen.

Today, using Big Data means that we can recover all the information on the machines and their environments via sensors installed on the production line. In order to process this massive data flux, using AI and Machine Learning together will allow us to detect every weak signal. We can, then, take action on the causes rather than the consequences.

The result? Maintenance is performed before breakdown can occur, and the production line isn’t put at risk.

Companies such as Tetra Pak are already carrying out in situ predictive maintenance tests, as demonstrated by the tests performed on the production lines of 17 clients. The company can monitor more than 5,000 machines across the world in real time, using Microsoft’s Azure for storing and analysing the data.

At the slightest “weak” signal sent by the machines, Production Managers receive an automatic notification. They then have the time and the leeway they need to carry out repairs on the affected machines. During the 6 month test, more than 48 hours of uptime were saved on each line.

In 2017, for this particular company type the implementation of a data collection and analysis system is a major issue. Predictive maintenance offers only positive effects, with greater productivity, lower risk of failure, and of course, increased profitability.

If you are interested in predictive maintenance, why not check out our webseries “SoDigital”? In our Big Data special, our expert presenter offers Sopra Steria’s outlook on the subject.

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