10 key things to look for when evaluating prognostic products to forecast machine failure

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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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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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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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Prognostics is the future of condition monitoring, telling you when your machine will fail AND what condition it is now in. Download our FREE white paper!

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Industry 4.0 promises many things around security and interoperability but the most interesting thing is predictive maintenance, enabled by prognostics.

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