Industry 4.0 technology enablers in the fluid power industry
Industry 4.0 technology enablers are a group of diverse technologies — spanning machine learning and robots to additive manufacturing and cloud technology — which work together in an ecosystem.
And yes, I know these technologies sound like they would more broadly supplement the fluid power industry than impact it directly, which brings to the fore the question as to how Industry 4.0 does in fact impact the industry. It’s a fair question which depends on the enabling technologies, as some are more relevant than others.
When it comes to, for example, cloud technology, we use it but don’t make it. This also applies to robots which we don’t produce but use in our manufacturing.
So, which of these technologies is going to be of more interest? From our perspective, only a combination of technologies will work as a solution for Industry 4.0. And within these technologies, predictive maintenance is of the most interest as an outcome, because it’s where technology can have the most impact.
Broadly speaking, the different types of maintenance include reactive maintenance, which centres around repairing breakdowns when they occur, and preventive maintenance, which centres around mobile or stationary machine service interventions at scheduled intervals before breakdowns occur.
In contrast, 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 — then to apply this knowledge to the present and then to predict a future state.
This has traditionally been called ‘condition monitoring’ in the context of industrial applications. But 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.
Predictive maintenance is different to what we previously called condition monitoring, which basically looks at the current state of the machine and takes readings on it. Predictive maintenance instead hinges on the technology of accurate forecasting. Another difference is that more is done with the information gleaned. 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.
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.
But what are the benefits of predictive maintenance? There’s obviously the question of whether real-time condition monitoring has a greater benefit and return on investment, taking into account the cost of training people and placing sensors onto machines.
The answer here is a definitive yes, as long as the predictive maintenance works — keeping in mind that anything applied runs the risk of not working.
To work it needs buy-in from all business personnel. A common problem with predictive condition monitoring is that a slot set aside to service a machine is sometimes cancelled due to the production department piping up that it needs the machine to keep production schedules. However, if the maintenance department has the authority to adhere to plans, then data collection performed will bear fruit and there will be a return on investment.
The next question is why would condition monitoring be required on a machine that is more reliable as a result of the processes in place to ensure its reliability?
Here the important point to note 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 at some time. The advantage of the sensors is they enable the collection of data for more accurate machine predictions.
Through my training I’ve discovered that what works is to get people thinking about maintenance in the first place, which revolves around mechanics repairing machines in the Australian setting. However, of fundamental importance is the idea that the function of the maintenance department is to maintain machines — otherwise it would just be called the repairs department. So it’s all about having the knowledge, skills and technology to be reliable.
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