2019 marks an inflection point in the maturity of Industry 4.0 and the application of real-world predictive maintenance. As a provider of industrial predictive maintenance analytics to Fortune 500 companies, it is very much our area of expertise. Read our predictions for 2019.

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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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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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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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Prognostics has seen limited adoption for a number of reasons. When built upon condition monitoring it provides a solid foundation for predictive maintenance.

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The hype around Predictive Analytics seems never ending. Yet it has some serious limitations when it comes to predicting machine failure and avoiding downtime.

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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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Prognostics is clearly the future of condition monitoring but how does it help predictive maintenance? By providing the Remaining Useful Life of your assets..

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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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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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Condition monitoring is a manual process that doesn't scale. With cloud computing and the advent of prognostics, condition monitoring is best left to machines.

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A number of prognostics tools are turning up but how effectively can they help you with your predictive maintenance? We've put together a checklist to help!

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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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Prognostics is a key element of predictive maintenance and the most exciting thing to be enabled by Industry 4.0. This is what you need to know.

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

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Predicting when a machine will stop being able to perform its given function has been held back by some old beliefs that we are changing with PROGNOSYS.

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Take an iterative approach to prognostics and condition monitoring by following our golden ‘3 Es’ principal of Establish, Exploit and Enhance!

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Condition monitoring is becoming more powerful by adding prognostics with PROGNOSYS, here's how

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Simply put, prognostics is the art of being able to accurately forecast when a component or machine will fail. It can be easy to confuse it with condition monitoring but it differs in the way that condition monitoring tends to focus more on the alerting of the here and now state of the machine, identifying failure as it is happening. It’s great when you know what you’re looking for but often the failures that catch you out and lead to downtime are things that you never expected to see – your condition monitoring system then become next to useless.

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