Predicting the future

The fourth industrial revolution is well underway and as state-of-the-art technology drops in price, many more industries are benefiting from smart factories. As a recent PwC survey found 72% of companies expect to achieve advanced levels of digitisation by 2020, the reach of these technologies is only set to increase in 2018.

Here, Nick Boughton, sales manager at Boulting Technology shares his predictions for 2018. In November, the UK Government announced that 2018 would be the Year of Engineering. This coupled with the launch of the Government’s Industrial Strategy and Made Smarter Review has signalled a major vote of confidence in the sector as it pledges to help make the UK a world leader in the Fourth Industrial Revolution by 2030.

With Industrial Digital Technology’s playing such a significant role in the transformation of the sector, what should manufacturers be investing in?

Real applications of virtual reality

Virtual reality (VR), which digitally simulates a product or environment and augmented reality (AR), where the digital product or information is projected on to a real-world background, have traditionally been consumer-focused applications, aimed mostly at gamers.

However, with equipment such as the Microsoft HoloLens now being aimed purely at business applications, this is changing.

Boulting Environmental Services uses virtual reality to provide its clients with a unique opportunity to immerse themselves in their projects, develop designs more clearly prior to beginning construction work and reduce mistakes.

Virtual reality will become prominent during the design of a facility and it could even have applications for building information modelling (BIM). Inputting computer aided design (CAD) files into a VR application can allow the designer, engineer and client move around the product and facility, viewing it under a different light without the need to produce expensive prototypes. VR also has the potential to revolutionise training, particularly when working in hazardous environments. Engineers can explore and mange a range of scenarios without any risk to themselves or equipment.

Maintenance is where augmented reality comes into its own. AR can instantly provide important information to maintenance engineers wearing AR headsets, while allowing them to keep their hands free.

For example, when combined with remote monitoring and dashboard user interfaces, the status of a drive or motor control centre can be visualised next to the system in question. This type of technology is already being employed by companies with multiple sites, allowing for the comparison of key performance indicators (KPIs) between plants, learning from one-another to improve process efficiency and asset lifespan.

Similarly, when combined with a risk based maintenance schedule such as Boulting’s BRISK, each piece of machinery can be colour coded according to the risk it poses to the plant.

The rise of artificial intelligence

Machine learning is a concept that has been around for decades, where the computer doesn’t rely on rule-based programming, but instead operates using algorithms that can adapt and learn from data.

Closely related to this is artificial intelligence (AI), a branch of computer science aiming to build machines capable of intelligent behaviour. One of the major benefits of AI is advanced data analysis, where data is collected, stored and analysed automatically.

Nick Boughton

Dependant on the results of the analysis, processes can be automatically altered, increasing productivity, reducing costs or even preventing production downtime. Combined with trend prediction and predictive maintenance schemes, efficiency and yield rates can be greatly increased across a […]

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Predicting the maintenance

It happens at the worst of times – late for a meeting, on the way to the rugby and even when you’re desperate for the bathroom. When your car breaks down, you can moan in retrospect, acknowledging the signs that it needed urgent maintenance. Thanks to technology, more specifically the evolution and application of cognitive learning, these frustrating occurrences will become a thing of the past.

Connecting the things

Analyst house Gartner forecasts that there will be 20.8 billion connected ‘things’ worldwide by 2020. Enterprises that stick to an old ‘preventive’ data methodology, says Mark Armstrong, managing director and vice-president International Operations, EMEA & APJ at Progress, are going to be left behind, as this approach accounts for a mere 20% of failures.

Predictive maintenance brings a proactive and resource saving opportunity. Predictive software can alert the manufacturer or user when equipment failure is imminent, but also carry out the maintenance process automatically ahead of time. This is calculated based on real time data, via metrics including pressure, noise, temperature, lubrication and corrosion to name a few.

Considering degradation patterns to illustrate the wear and tear of the vehicle in question, the production process is not subject to as high levels of interruption without the technology. By monitoring systems ‘as live’, breakdowns can be avoided prior to them happening.

It’s no longer a technological fantasy. Due to data in cars being collected for decades, researchers and manufacturers can gather insights that could be used to prepare predictive analytics. This will assist in predicting which individual cars will break down and need maintenance.

Now that the Internet of Things (IoT) is a reality, car manufacturers can use this information to offer timely and relevant additional customer services based on sophisticated software that can truly interrogate, interpret and use data. So who is going to be responsible for taking advantage of this technology?

Bolts and screws

Key management figures in the transport industry must commit to a maintenance management approach to implement a long-term technological solution. As described by R.Mobley, run-to-failure management sees an organisation refrain from spending money in advance, only reacting to machine or system failure. This reactive method may result in high overtime labour costs, high machine downtime and low productivity.

Similarly reactive, preventive maintenance monitors the mean-time-to-failure (MTTF), based on the principle that new equipment will be at its most vulnerable during the first few weeks of operation, as well as the longer it is used for. This can manifest itself in various guises, such as engine lubrication or major structural adjustments. However, predicting the time frame in which a machine will need to be reconditioned may be unnecessary and costly.

As an alternative option, predictive maintenance allows proactivity, ensuring lengthier time between scheduled repairs, whilst reducing the significant amount of crises that will have to be addressed due to mechanical faults. With a cognitive predictive model, meaning applications are able to teach themselves as they function, organisations will be able to foresee exactly why and when a machine will break down, allowing them to act […]

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IoT power calculator helps IoT developers by predicting battery life

IoT power calculator helps IoT developers by predicting battery life

Premier Farnell has created an IoT Power Calculator that helps developers of IoT projects to understand the expected battery life of their IoT devices, and allows them to experiment with different components and software algorithms to determine their impact on battery life.

Battery power is in increasing demand in IoT applications incorporating sensor systems that collect data and pass the information to the cloud. Unlike many larger connected systems, these relatively small devices often do not have access to mains power. This means that they must have a means of powering themselves, something that is achieved using either batteries or energy harvesting.

Although an increasing number of applications can now be developed at the ultra-low power levels required for energy harvesting, many more are not suitable for this approach, and in such cases batteries are needed to power the system. Unlike products that use energy harvesting, batteries will need changing at some point, and with the cost of replacing batteries often higher than the cost of the IoT device itself, calculating battery lifetime is critical.

The calculator is easy to use and very quick to produce a result. Users enter basic parameters about their hardware – including different types of microcontroller and batteries – and their software (how frequently the software wakes, and how many cycles the data capture/processing and communications operations require) and the calculator uses these inputs to work out the power consumption.

The result is a fast and convenient way to quickly estimate battery life for an IoT application: something that was previously difficult and time consuming, involving either the creation of a spreadsheet or manual calculations of battery life.

Steve Carr, Global Head of Marketing for Premier Farnell said:

“We have a strong track record of working with design engineers to help them address the ever-changing design demands being created by an increasingly “connected” world. The Battery Life Calculator has been developed as tool to help address one of the critical challenges that need to be addressed when designing for IoT applications, and is just one of the many resources available in our Internet of Things Hub.”

Access Premier Farnell’s IoT Power Calculator.

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AI falls on the final furlong in predicting Kentucky Derby winner

The Kentucky Derby, one of the three races which make up the Triple Crown in US horse racing, was won over the weekend by Always Dreaming, the 9-2 favourite which came home by 2 ¾ lengths in cool and damp conditions. Yet for one company which placed its collective AI efforts on predicting the runners and riders correctly, Always Dreaming did not feature in their winners’ enclosure – despite getting the result spot on last year.

Unanimous AI, founded in 2015, offers what it calls ‘swarm AI’ technology which “amplifies human intelligence, empowering groups to harness their collective knowledge, wisdom and intuition by forming real-time AI systems,” in the company’s own words.

In other words, Unanimous AI aims to build a ‘super expert’ harnessing human capabilities with machine data usage. Partnering with TwinSpires, a horse racing betting firm, the aim was for not just a combination of individual race picks from the experts, but building an super expert and having them ‘think together’ as a real-time swarm moderated by AI algorithms.

Last year, the company made headlines by correctly predicting the superfecta – the top four horses in exact order – at odds of 542 to 1. This time round, the company’s picks were Classic Empire, which finished 4th, McCraken (8th), and Irish War Cry (10th). Always Dreaming was ranked fourth by its system, while Lookin at Lee, a 32-1 outsider, confounded the experts by finishing second – with the odds of the superfecta as a result being a whopping 76,000 to 1.

Prior to this year’s race, the company, led by Louis Rosenberg, was confident yet cautious about its chances. “While predicting sports always involve a large element of chance, Unanimous AI taps the intelligence of groups and evokes the best possible prediction based on the available information,” he said. “We have seen this work in a wide range of fields, from forecasting movie box office to predicting the price of Bitcoin. We are excited to see how these handicappers do against one of the most unpredictable of events.”

This time around, the post-mortem was more circumspect, although the company noted the odds were more significantly against them this time around, as well as adding it had still placed more horses than the average individual expert.

“The swarming process amplified the intelligence of the experts, boosting the average performance from 1.6 horses correct up to 2.0 horses correct. That means the experts would have been better off, as a group, going with the swarm than going with their own individual picks,” the company noted. “But without 32-1 Lookin at Lee in anyone’s forecast, the players’ pool missed out on the massive superfecta.”

You can read the full post from Unanimous AI here.

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