Predictive maintenance: a technology enabler for fluid power
Industry 4.0 has been a buzzword in the fluid power industry for the past few years. However, to date the uptake of solutions that could help companies become more competitive, efficient and productive using Industry 4.0 technology enablers has been slow.
According to HYDAC Technical Training Manager Paul Marley, only a combination of these technologies — spanning machine learning and robots to additive manufacturing and cloud technology — are of interest to the fluid power industry, with predictive maintenance being the most important.
Predictive maintenance service work is done on an as-needed basis, with the assessment as to when to intervene based on sensor technology.
“It’s all about predicting a machine’s future state, which in a nutshell entails collecting data on how the machine behaved in the past to apply to the present and then to predict a future state — a form of time travel. This has traditionally been called ‘condition monitoring’ in the context of industry,” Marley said. “And yes, condition monitoring has been around for a while, with the question arising as to what has changed to enable it to form part of Industry 4.0. From the outset it does simply sound like a change of terms and therefore not of much interest to us. But it’s a lot more than that.”
The difference between predictive maintenance and condition monitoring
Marley says that predictive maintenance is different to condition monitoring — which basically looks at the current state of the machine and takes readings on it — as it hinges on the technology of accurate forecasting. Another difference is that more is done with information gleaned, he said.
“Traditionally it was about recording the state of the machine, but now with Industry 4.0 it’s about collecting data on the machine passively all the time and in real time, which was not the case previously.
“And the fact that information is recorded in real time means that the period between readings is less, enabling increased accuracy when assessing the machine and pinpointing failures at their starting point.”
This can be applied to all machines and not only new machines.
“It’s important to note that putting sensors on an old machine that is always breaking down isn’t going to reduce breakdowns,” he said. “In this case maintenance practices and lubricant storage have to be repaired and users, operators and maintenance personnel have to be trained.”
As to condition monitoring, it has expanded what’s possible in this domain, with the accuracy of information coming through providing answers that differentiate Industry 4.0 from Industry 3.0.
Benefits of predictive maintenance
Marley underscores that condition monitoring has benefit and return on investment, taking into account the cost of training people and placing sensors on machines.
He cautions though that buy-in is needed from the team to prevent scenarios arising such as the production team overriding machine servicing to adhere to production schedules.
“Another question is: why would condition monitoring be required on a machine that is more reliable as a result of processes in place to ensure its reliability? Here the important point to note is that it’s not as simple as addressing reliability first and then putting sensors on a machine.
“The idea is that once a machine has become reliable, putting sensors on it and collecting data helps to predict future performance, taking into account that all machines need interventions and repairs. The advantage of the sensors is they enable the collection of data for more accurate machine predictions.”
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