Reducing unplanned downtime in factories is crucial to reducing overall maintenance spend and the total cost of asset ownership. Achieving this at scale however is a significant challenge and can only be achieved through the intelligent use of machine learning driven predictive maintenance solutions

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There are subtle but crucial differences between detection, diagnostics and prognostics when discussing machine health. Whilst you don't need to know the details, it's important to understand the differences to apply to your own industrial condition monitoring project for maximum benefit and ROI.

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The Internet of Things has a lot of promises associated with it, relating to how much it will improve our lives. Manufacturing will see the the best improvements.

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Industry 4.0 has often been critisized from a security point of view but is this really fair? Often the security issues such as Meltdown and Spectre are within.

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2017 was an exciting year for Industry 4.0, with it starting to gain some mainstream press attention. The Senseye founders give their outlook for 2018.

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Is Industry 4.0 secure?

November 14, 2017

The security of the IoT / Industry 4.0 is a HUGE topic. Here we distil the really important things that you need to consider when exploring this topic.

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Prognostics is particularly exciting as it means understanding the Remaining Useful Life of your machinery however it's easy to get wrong and be unsuccessful.

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Analysing condition monitoring data manually is beneficial but this method limits scalability whilst coming with great expense. Automated is best but how / when

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Maintenance practises have changed significant in a fairly short time. No planning has given way to scheduled planning, giving way to predictive maintenance.

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Investing the correct amount in condition monitoring can be a challenge as it's easy to spend too much and be disappointed with the results.

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