£1m R&D project to develop predictive maintenance for manufacturing
SamsonVT has announced a £1.3m research and development project to develop predictive maintenance for SMEs.
The Industry 4.0 start-up has partnered with RS Components to produce the first affordable machine learning-enabled predictive maintenance (PdM) solution for manufacturing SMEs.
“We know that the high costs and complexity of PdM tools are a big barrier to adoption for SMEs – which make up the majority of the UK’s manufacturing companies,” explained Sam Burgess, CEO at SamsonVT.
“But, by delivering a PdM platform that is accessible and affordable – leveraging cutting-edge machine learning techniques – we can help save British manufacturers billions every year in unplanned machine downtime.”
The partnership will be supported by Innovate UK, which has awarded a joint R&D grant of £805k to support the technology’s development.
“We are excited to be working with SamsonVT on this project. For busy SMEs that may not have previously considered PdM as an option, due to perceived cost and limited management time, this will be a real gamechanger,” added Richard Jeffers, Director, Maintenance Solutions at RS Components.
“This will provide them with a PdM solution that is as close to plug-and-play as you are going to get, generating the insights they need in order to know when to act and, just as importantly, when not to.”
The project will focus on the application of anomaly detection for improved maintenance, engineering and decision making.
SamsonVT will integrate data extraction, criticality assessment, machine learning (ML) and root cause analysis with its existing condition monitoring platform, SamsonBASE.
This will lead to the creation of a PdM platform which can use harvest and process relevant data, in order to detect anomalies within machinery.
Machinery downtime costs British manufacturers an estimate £180bn every year, or 3% of all working days.