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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To date, the manufacturing sector has benefited minimally from predictive maintenance due to difficulties with the scalability of manual analysis. Senseye changes this - cloud based predictive maintenance solution, with a clear, concise user interface.

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It's crucial to get the data correct for a predictive maintenance project in order to be able to accurately detect machine failure. 6 tips on getting good data.

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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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Servitization allows you to get closer with your customers by providing your product as a service. Predictive maintenance helps ensure that you can deliver it.

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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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Predictive maintenance can result in avoiding between 30-50% of downtime that occurs during preventative maintenance but not many companies have adopted it.

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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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It's easy to go wrong in deploying an IoT solution. We've put together three principles to help in building an effective solution to avoiding machine failure.

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The crucial differences between Preventative and Predictive Maintenance and how you can save downtime and increase productivity when you forecast machine failure

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