The Sick Safety Machine Analytics displays a live visualisation of one or more machines so users can monitor the status of safety processes in real time, investigate the root-causes of stoppages, and take steps to eliminate them.

The specially-developed Sick software supports improved Overall Equipment Effectiveness (OEE) by acquiring data from safety sensors, then displaying interrupts in real time, as well as a graphical representation of system trends.

Quickly and easily installed on any device with a web browser, the Sick Safety Machine Analytics can be linked to multiple safety sensors or actuators, independent of hardware. Users get started by uploading images of their machines, then overlaying safety sensors onto the visualised scene to facilitate easy navigation of real-time data.

With access rights set by the administrator, the system can then provide instant feedback, as well as longer-term historical analysis for maintenance and production management.

Industry 4.0 for Safety Analytics

“The benefits of extracting data from sensors at the heart of machines have become well accepted as part of Industry 4.0 condition monitoring, however, for some users, connecting to safety systems may still feel daunting,” said Dr Martin Kidman, Sick UK machinery safety product manager.

“Sick Safety Machine Analytics is a simple and secure way of taking a virtual tour around the safety systems on your machines to look for any weak spots. You can drill down and identify any interrupts, so they can be quickly rectified. Once connected to a machine, this system offers a low-investment opportunity to identify where gains can be made in productivity by increasing machine uptime.”

Data from safety sensors and actuators, connected to a Sick Flexi Soft safety controller, is routed via a dedicated gateway to a separate web-enable device such as an industrial PC, tablet, smartphone or laptop.

Set Key Performance Indicators

Using the stored data, users track system history using their own Key Performance Indicators, for example, to compare a daily analysis of system downtime with patterns over a longer time period to get a better understanding of overall system performance. Alternatively, data for individual components can be investigated, e.g. to monitor a safety proximity sensor to see how often a door was opened or closed.

Any information available via the Sick Flexi Soft controller can be sent to the Safety Machine Analytics software over a standard Ethernet connection. All safety events are stored in the event log table to enable detailed analysis and to identify system improvements.

For more information about the Sick Safety Machine Analytics software, visit www.sick.co.uk