Prognostics powered by machine-learning cuts factory downtime in half without the need for data scientists
As manufacturers continue to automate their factories and connect them with intelligent sensors, the data collected is providing critical information on the health and remaining life of their machinery, enabling scalable predictive maintenance.
The idea of understanding the health of machinery, otherwise known as condition-monitoring, is nothing new. As a principle, it’s been around for generations, but online technology saw widespread adoption in the 1990s in the aerospace industry.
Unfortunately, performing it at industrial scale has remained a pipedream for everyone other than the privileged few who have been able to afford the sky-high costs of scaling it up, and employing an army of expensive monitoring and data experts to extract the critical insights.
Prognostics goes beyond condition-monitoring by showing the remaining useful life of machinery. As manufacturers delve further into Industry 4.0, they are increasingly seeking these techniques to reduce costly downtime. Fortunately, the democratisation of enabling technologies, such as cloud computing and the internet of things (IoT), is now set to accelerate these from their limited-scalability phases.
“Prognostics software takes in data from Industry 4.0 machinery and automatically builds a picture of machine health as well as the remaining useful life,” says Alex Hill, chief commercial officer at Senseye, a young UK company whose machine-learning-based software for predictive maintenance already helps several Fortune 100 companies prevent downtime from machine failure on their production lines.
“By automating condition-monitoring and prognostics analysis, you can do predictive maintenance at scale, so you know which machines are currently healthy and which aren’t, and you know which ones will be healthy or not in a given timeframe.”
Senseye helps manufacturers avoid downtime and save money by automatically forecasting machine failure. Its unique machine-learning algorithms allow it to be used on any machine from any manufacturer, taking information from existing industrial IoT sensors and platforms to diagnose failures automatically and provide the remaining useful life of machinery.
If unplanned downtime happens, for example in the automotive industry, every minute costs £50,000, Mr Hill says. “It’s an incredible amount of money that our customers want to avoid losing; if they have a downtime event they can never make that back. By using our technology, industrial companies have reduced downtime by 50 per cent in three months. It’s incredibly quick to see results.”
Central to Senseye’s approach is cloud-based technology that doesn’t require experts in condition-monitoring or data science to operate. It’s designed to be used by the maintenance team, not IT staff.
Senseye helps manufacturers avoid downtime and save money by automatically forecasting machine failure
Previously, predictive maintenance was so difficult and expensive to do at scale because of the need for these skills. By not relying on human expertise to analyse the data, manufacturers can now enjoy predictive maintenance at scale, going from a few machines to a few thousand without requiring
“We do all the difficult analysis work for them and they just get information about what machines are causing problems and how long they’re likely to live,” says Mr Hill. “We connect with whatever data source is already being used so installation can take anything from half an hour to two weeks, depending on what’s in place, making the whole set-up process as painless as possible.”
In the coming years, predictive maintenance is not only poised to reduce factory downtime drastically, but it will also enable servitisation in industrial sectors. The concept has already transformed the aerospace industry by changing the business model of many aircraft manufacturers, for example, from physically offloading assets to selling and delivering flight hours.
The evolution of servitisation will mean industrial manufacturers will begin to sell machine capability rather than machines themselves. For example, instead of the manufacturer selling a welding robot, it will sell a number of welds per hour, moving from a model of selling hardware to offering products as a service.
“Delivering a product as a service requires a high degree of automated data analysis when you’re looking at the industrial scale of hundreds or tens of thousands of machines,” says Mr Hill. “Predictive maintenance, without large deployment or ongoing costs and with a return on investment of months, now really is achievable.”
For more information please visit senseye.io