Chosen by top UK-based entrepreneurs, the Startups 100 Judges’ Picks have been selected to reflect some of the top trends in this year’s list.

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Condition monitoring has evolved over the past 30 years.

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Senseye announced today that it has recruited Wakako Yamaguchi to expand its business activities in Japan. Wakako will lead Senseye’s sales activities in Japan and provide account management support to its customers in the country.

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Senseye announced today that it has joined the Partner EcoSphere at OSIsoft to deliver its automated condition monitoring product to help PI System customers on their digital transformation journey.

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Senseye, a provider of predictive maintenance software, today announced that it has appointed Peter Livaudais to lead its operations in France, Belgium, Netherlands, Luxembourg and the French-speaking areas of Switzerland.

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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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Today around two-thirds of manufacturers are gathering data from their production environments, yet relatively few are using it to improve processes or boost productivity and yield, argues Dr Simon Kampa.

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For manufacturers, leveraging data and analytics can deliver substantial returns. We’ve helped large scale manufacturers halve their levels of unplanned downtime and cut their maintenance costs by around 40%. The reduction in unplanned downtime tends to be the most significant saving here. Every minute that critical machinery is offline can cost big factories tens of thousands of pounds, so the returns that can be achieved by preventing this are substantial.

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Senseye, which was made a MindSphere Gold partner in August 2018, first provided its software to MindSphere users in June 2018, when it was made available as a complementary service that could be connected to the operating system. This new version of Senseye’s application was developed specifically for MindSphere and is hosted within the operating environment itself.

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Senseye, which uses machine learning to automate condition monitoring and prognostics analysis, has translated its industrial operations software tool into five additional languages. Previously available in English only, Senseye can now be used on the shop-floor by maintenance and operations people that speak French, German, Japanese, Russian and Spanish to cater to existing customers, with additional languages being added.

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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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Senseye, a provider of predictive maintenance analytics, today announced that it has partnered with the North East Automotive Alliance (NEAA), to provide its predictive maintenance software to the automotive sector in the North East of England.

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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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Experienced sales leader joins Senseye as Managing Director for DACH region.

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Senseye, a provider of predictive maintenance analytics, and Iconsys, the manufacturing systems integrator, today announced a new alliance to help industrial companies boost operational efficiency and profitability using Industry 4.0 technologies.

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Factories are no longer simply a mass of machinery operating as a series of siloed production lines. Instead, manufacturing executives and engineers manage interconnected networks of moving parts, something more akin to a living organism, that can be trained and fine-tuned to optimise performance.

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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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Smart factories are rapidly becoming the future of manufacturing, offering a new level of efficiency and productivity to those investing in them. Industry 4.0, combined with increasingly sophisticated analytics, is playing a huge role in driving the smart factory movement.

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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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Senseye, a provider of predictive maintenance analytics, today announced that it has tripled the size of its dedicated customer success team to accelerate the returns that customers can make from investing in its technology.

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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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Our CEO, Simon Kampa, recently featured in IMPO magazine where he provides a five-point plan outlining how manufacturing environments of all shapes and sizes can achieve substantial improvements in productivity and efficiency by implementing predictive maintenance at scale.

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Senseye, a provider of predictive maintenance analytics, today announced that it is rolling out new Trend Recognition algorithms capable of automatically identifying machine problems at an earlier stage than was previously possible.

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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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Our CEO, Simon Kampa, recently featured in IMPO magazine where he explains how artificial intelligence has helped increase the adoption of predictive maintenance by manufacturers and helped to fill maintenance skills gaps and boost productivity.

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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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Our CTO, Robert Russell, features in August’s edition of Processing Magazine where he highlights the operational challenges of scaling predictive maintenance & how automating analytic tasks allows organizations to expand the coverage of its assets without significantly increasing costs.

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Our CEO, Simon Kampa, recently wrote an article for Smart Industry which explores the challenges of deploying traditional predictive maintenance at scale, and how artificial intelligence is enabling wider and faster deployment.

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Senseye, a provider of predictive maintenance analytics, today announced that its award-winning machine condition monitoring and prognostics software has been added to the Manufacturing Technology Centre’s Factory in a Box product, delivered through the Smart Manufacturing Accelerator.

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Senseye, a provider of predictive maintenance analytics, today announced that it has now doubled the size of its engineering workforce since closing a £3.5 million Series A funding round in December 2017...

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Senseye, a leading provider of predictive maintenance analytics, today announced a new partnership with Siemens to make Senseye’s award-winning condition monitoring and prognostic software available to manufacturers through the MindSphere Industrial Internet of Things operating system.

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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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For 2018, Senseye is unveiling several exciting upgrades to its award-winning predictive maintenance software, further automating machine prognostics analysis.

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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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Senseye, the predictive maintenance leader is please to announce that it has closed £3.5m in Series A funding. Led by MMC ventures, with existing investors participating.

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Senseye has joined forces with PTC to offer easy to use predictive maintenance to users of the leading ThingWorx® industrial innovation platform

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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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Senseye is excited to announce that it will be demonstrating its award-winning scalable predictive maintenance solution, live at Connected Manufacturing 2017.

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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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Senseye, the scalable predictive maintenance product will be demonstrated live at Sensors & Instrumentation at the Birmingham NEC.

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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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Senseye, the Uptime-as-a-Service company celebrates its achievement of over 1000 machines under automated prognostics analysis for predictive maintenance

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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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Senseye, the Uptime-as-a-Service company celebrates its recognition as a leading provider of automated predictive maintenance with a discount offer for June

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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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Senseye, the manufacturing Uptime-as-a-Service leader for predictive maintenance, will be exhibiting at Maintec 2017 in Birmingham, UK in March.

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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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Senseye, the Uptime-as-a-Service leader today announced the launch of the next generation of predictive maintenance software to include remaining useful life

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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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Senseye, the Manufacturing Uptime as a Service company, today announced the closure of a significant seed funding round to further capture prognostics market.

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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 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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With the IoT helping predictive maintenance, do human diagnostic engineers have a place in predicting machine failure and if so, what is it?

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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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Steve McEvoy, a leader in prognostics and condition monitoring, formerly with GE Aviation Systems, has joined Senseye’s advisory board.

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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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Senseye is working with Parvalux, a Europe's leading motor gearbox manufacturer to predict when machines will fail, automatically, using PROGNOSYS

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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, provider of the PROGNOSYS infrastructure-free prognostics and condition monitoring solution to the manufacturing industry, has today announced a strategic partnership with Momenta Partners, advisors to the Industrial Internet of Things (IIoT) market.

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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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Senseye announces the release of their ground-breaking software as a service solution. Senseye helps manufacturers maximize Overall Equipment Effectiveness (OEE) and save expenses for manufacturing businesses by reducing machine downtime.

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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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