Predictive maintenance is surely one of the most frequently noted use cases for Industry 4.0. And it’s easy to see why. Any time a machine has downtime, it delays production and costs money. This is especially the case when the downtime comes unexpectedly and the equipment operator needs to respond –but is unable to.
It often doesn’t require too much information to quickly recognize errors. For instance, increased vibration in a drivetrain could be a sign of a worn-down bearing. And, in the case of a conveyor belt, a rise in starting current could be the result of increased friction and could signal an equipment malfunction. These examples also demonstrate why experienced plant operators can “sense” when their machinery or plant might be facing a problem.
Through the use of cloud technology, it has become much easier to convert these experiences into specific algorithms that make the corresponding predictions about the condition of the machinery. These are often based on relatively few data points – speed, vibration, temperature, etc. But how do you obtain this data in the first place?
If the data is also needed for the system controller, then it can simply be accessed from the PLC. But that is rarely the case. The process often requires additional sensors, which first have to be installed in the machinery. And this is where PROFINET’s most powerful feature comes into play. In a PROFINET network, further Ethernet devices can be installed almost anywhere. PROFINET’s open technology makes it possible, for instance, to add sensors with an OPC UA interface, which send their data directly to corresponding cloud services or edge gateways, without needing to tediously reconstruct the automation solution. IO-Link also makes use of this technological feature, when the IO-Link master sends the sensor data both via PROFINET for controlling and via OPC UA to IT systems. This is yet another example of how perceptive the PROFINET architecture is – making it perfectly equipped for Industry 4.0 applications!
