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 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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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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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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Reducing machine downtime can come through condition monitoring (current health) and prognostics (remaining useful life), key for predictive maintenance

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Different techniques come under the label 'condition monitoring'. In this overview we explore where it came from, what is done and how effective it is.

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Prognostics is a relatively new term in industry and it's key to predictive maintenance. It all comes down to calculating the remaining useful life of machines.

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