To receive reliable automation forecasts, the developers from SSV Software Systems GmbH based in Hannover, Germany, have designed an added predictive maintenance feature for their IoT technology stack Thinglyfied that can record comprehensive machine and environmental data on site in real time. By analyzing the latest condition data, individual modules can forecast potential machine failures and production stoppages. A Things Connector module can be used to read the necessary data from a PLC via Modbus, ISO-on-TCP or PROFINET, for example, and this data can be combined with other measurement data from external sensors.
At HANNOVER MESSE 2016, SSV Software Systems will be explaining how the data obtained in this process is forwarded to the predictive service of a public cloud online. Thinglyfied is completely flexible in terms of cloud and provider, as practically all standard platforms will be supported with relevant services. Condition monitoring data can also be generated and viewed for the entire system on site via the Device 2 Cloud and Device 2 App modules of the IoT technology stack. An OPC UA server and Bluetooth-based micro gateway plus smartphone app are available for this purpose.
A typical scenario where Thinglyfied predictive maintenance could be used is in updating and digitalizing a reactive service business process in mechanical and plant engineering scenarios. For example, maintenance deadlines that are necessary as a result of machine/system usage can be coordinated automatically by analyzing the data in the service cloud and added to the schedules of the relevant employees. This enables the necessary spare parts to be procured in advance and in good time for maintenance on site.
SSV Software Systems GmbH (30419 Hannover, Germany), Hall 17, Stand A18/2, Topic: Predictive maintenance pavilion, co-exhibitor with Predictive Maintenance 4.0.