Representative End User Clients
Representative Automation Clients
Representative Software Clients
I've recently been in many Industrial Internet of Things (IIoT) discussions where improving the uptime of large rotating machines was mentioned as low-hanging fruit for asset performance improvement. The examples given are remote condition-based monitoring examples, where bearing temperature or vibration sensors are added to machines to feed monitoring and analytics software and systems. And these are indeed very good examples of IIoT opportunities.
But maybe there's a better way?
What if the bearings themselves could detect problems and communicate wirelessly? And what if the bearings could take advantage of the inherent rotational energy to self-power the electronics?
I've just described SKF Insight, a new bearing health management technology which uses a self-powered, intelligent wireless sensors in the SKF bearing to provide instant condition monitoring data via the Internet. SKF Insight monitors dynamic parameters such vibration, temperature, lubrication condition and load, and informs the user when conditions are abnormal and can threaten to cause bearing damage. This technology is now undergoing trials in projects in the wind turbine and railway industries. Traditional condition monitoring can detect bearing damage, but with SKF bearing health management algorithms applied to the dynamic data, SKF Insight can spot the abnormal conditions that cause damage and can take actions to prevent it.
One promising application for SKF Insight is in wind turbines. In some cases, changing the main bearing on a wind turbine is so expensive that it undermines the business case for building the turbine in the first place. SKF is working with a wind turbine customer to develop a solution. It measures dynamic bearing information in the true operating state, then wirelessly communicates it to remote monitoring centers or local maintenance crews. The solution will monitor bearing speed, vibration, temperature and lubrication condition. It can be also be retro-fitted to wind turbines already in operation. SKF is also developing a similar solution for wheel end bearings, to allow rail operators to see in real time how operating conditions are affecting the bearing and to improve maintenance schedules.
As this technology spreads to other large rotating machine applications, it will undoubtedly change maintenance and operating models - and performance expectations.
But maybe there's a better way?
What if the bearings themselves could detect problems and communicate wirelessly? And what if the bearings could take advantage of the inherent rotational energy to self-power the electronics?
I've just described SKF Insight, a new bearing health management technology which uses a self-powered, intelligent wireless sensors in the SKF bearing to provide instant condition monitoring data via the Internet. SKF Insight monitors dynamic parameters such vibration, temperature, lubrication condition and load, and informs the user when conditions are abnormal and can threaten to cause bearing damage. This technology is now undergoing trials in projects in the wind turbine and railway industries. Traditional condition monitoring can detect bearing damage, but with SKF bearing health management algorithms applied to the dynamic data, SKF Insight can spot the abnormal conditions that cause damage and can take actions to prevent it.
One promising application for SKF Insight is in wind turbines. In some cases, changing the main bearing on a wind turbine is so expensive that it undermines the business case for building the turbine in the first place. SKF is working with a wind turbine customer to develop a solution. It measures dynamic bearing information in the true operating state, then wirelessly communicates it to remote monitoring centers or local maintenance crews. The solution will monitor bearing speed, vibration, temperature and lubrication condition. It can be also be retro-fitted to wind turbines already in operation. SKF is also developing a similar solution for wheel end bearings, to allow rail operators to see in real time how operating conditions are affecting the bearing and to improve maintenance schedules.
As this technology spreads to other large rotating machine applications, it will undoubtedly change maintenance and operating models - and performance expectations.