Digital twins are virtual models of real life assets or production operations. They can be powerful weapons in the emerging armory of the Industry 4.0 but face a major hurdle in terms of scalability...

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What do your manufacturing customers really want? It’s certainly nice for them to have the most nimble robots or the fastest machines, but such assets are only a means to an end. What all organizations really want is a guarantee that they can keep their operations working at optimum efficiency to support their key business objectives. That's where servitization comes in.

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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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Whether you are new or already familiar with Predictive Maintenance (PdM) the Senseye team have put together a handy A to Z guide of some of the commonly used words and phrases associated with the maintenance practice.

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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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Servitization is becoming an increasingly common term for manufacturers around the world. However, a clear understanding of what servitization is & how it affects manufacturers is much harder to come by. We’ve produced a white paper to demystify the subject. For those on the go, here’s the TL;DR.

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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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Digitalization is the topic of the moment, and Industry 4.0 is at the heart of this for more efficient, cost effective factories. We're seeing an increase in the number of times this job title is cropping up and it shows a general increase in interest in businesses adapting for servitization.

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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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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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Industry 4.0 has often been critisized from a security point of view but is this really fair? Often the security issues such as Meltdown and Spectre are within.

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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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Is Industry 4.0 secure?

November 14, 2017

The security of the IoT / Industry 4.0 is a HUGE topic. Here we distil the really important things that you need to consider when exploring this topic.

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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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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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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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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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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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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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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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Implementing Industry 4.0 can be costly and distracting but it doesn't have to be. We've listed 4 things to help you keep on top of your Industrial IoT project

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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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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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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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How does the IIoT (Industrial Internet of Things) help Predictive Maintenance? It can be used for prognostics to help avoid downtime and save money - simple!

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Forecasting machine failure sounds great but there can be some prerequisites. Here's our top three for you to check off to simplify things.

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There's a lot of hype about the industrial IoT and some manufacturers of automation products think we are already there, but are we?

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It can be difficult to measure how much money predictive maintenance can save in manufacturing. Thankfully we;ve found a great and impartial case study

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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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Exploration of some bad advice for Industry 4.0 and how it can improve your manufacturing operations

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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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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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IoT meets Industry – Part 3

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IoT meets Industry – Part 2

November 30, 2015

IoT meets Industry – Part 2

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IoT meets Industry – Part 1

November 18, 2015

IoT meets Industry – Part 1

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Not Horizontal, Cross Vertical!

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Using Senseye with Thingspeak

September 14, 2015

Using Senseye with Thingspeak

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Time of the V1.0s

May 21, 2015

Like software and hardware products, aircraft have general maturity designations; A or Mark 1 for the first production model, B (or Mark 2) for a refined and typically upgraded model and so on, (if you're in an 'X' or it doesn't have a letter after it you know you're in for a wild ride with what is essentially an experimental beta aircraft).

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