Condition monitoring has evolved over the past 30 years.

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What is the next step in the world of predictive maintenance? The answer: prognostics. Prognostics is the science of forecasting when your assets will stop being able to perform their intended functions. With prognostics in place you can properly perform PdM which is undoubtedly the future of condition monitoring.

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The idea of predictive maintenance can be daunting for some manufacturers as it represents a significant cultural shift in how they plan, prioritize and perform maintenance activities. The benefits of doing so can be huge but if this shift isn’t properly managed, there can be serious consequences.

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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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In a competitive marketplace enhancing machine reliability by implementing a condition monitoring program can make all the difference. But realizing your ambitions takes planning, commitment & continuous improvement. We look at the common pitfalls & provide ways you can realize its potential.

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Making the decision to transition to automated condition monitoring shouldn’t be taken lightly. Far from a standalone maintenance project, automatic condition monitoring requires a shift in the entire organizational culture to support dramatic process, attitude and skillset changes.

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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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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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Condition monitoring is useful for understanding the current condition of an asset but requires lots of manual analysis to get value from. We're changing that.

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

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Senseye is focused in getting to the operational stage as quickly as possible and getting to the ROI evidence in months rather than years.

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Condition Monitoring (CM) is the process of monitoring data (vibration, acoustic emissions, temperature, etc) from machinery in order to identify changes which may indicate faults.

Condition monitoring project life cycles tend to follow a consistent traditional engineering roadmap from specifying the system through implementation, rollout, training and support.....

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