Maintenance is vital to keep your machines working correctly, qualitatively and safely. But when should you carry out maintenance? You can’t wait too long, because that might result in breakdowns. You also don’t want to do it too soon, because of the process cost. Can’t we determine the ideal time for maintenance? Let’s find out in this blog!
Different approaches
Let’s start with the most basic approach, reactive maintenance, in which you only react and carry out maintenance when something breaks. This approach maximizes the usage of a machine but can result in unforeseen breakdowns. In a production line reactive maintenance typically isn’t preferred, as an unforeseen breakdown can bring production to a halt.
An approach that deals with this is preventive maintenance, in which you’ll carry out maintenance on a schedule. The idea is to ensure nominal machine performance by doing maintenance before machines break. The great benefit here is that you can schedule when your machine will be unusable. The cost, however, is that you are performing maintenance early.
If you remember the title, you already know where this is going. To prevent the added cost of preventive maintenance, you can use machine learning to give insights in the state of the machine. Based on these insights, the predictive maintenance approach allows you to schedule maintenance only when it’s necessary.
Possible machine insights
There are many different ways to gain useful information for maintenance. One approach is based on failure classifications. Depending on how you frame this task, you can not only predict if a machine will fail within a time frame but also what type of failure it’ll be and what caused it.
But what if you want to have an exact prediction of a machine that could break down? Look no further. Failure regression allows you to predict when the machine will most likely fail and know well in advance when maintenance is necessary or how long you can postpone halting the machine.
Not all failures are predictable though. What if someone uses a machine the wrong way or something hits the machine in a vulnerable place? For this, you need to somehow know if your machine is working correctly right now. This task can be done through anomaly detection algorithms which will tell you at any moment if a machine is still working correctly or not.
A final category is survival analysis, which models the machine’s degradation process. This way you can gain insights in how the machine degrades and which activities or environments make it degrade faster or slower. This approach allows you to plan maintenance depending on the degradation level, as well as learn how to optimally use the machine. And by doing so, you increase the time between maintenance and make the machine more profitable.
Predictive maintenance requirements
Predictive maintenance all starts with data and the infrastructure to get this data. Any relevant data that indicates the correct functioning is interesting, including generic information from the machine, the gauges and other sensory equipment that is already available. People can see, hear or even feel if something is wrong or if the machine requires maintenance, like rust, creaking, or vibrations. These are all things that can be measured either directly or through virtual sensors, for instance by placing cameras with algorithms to detect rust or microphones for sounds and vibrations. This data corresponds to the state of the machine.
To learn how machines degrade, you also need to know how they have been used. Through work order and inventory usage data, for example, algorithms can learn that a period of heavy usage has a greater effect on the degradation than the holidays. More often than not, data from your CRM and ERP systems contain more useful information than the sensory equipment on the machines themselves, especially if you combine the data of all identical machines. That way you can reuse the same models on multiple machines and acquire more data to train and improve your models.
So how will you collect all that data? A good IoT infrastructure is going to be crucial. Installing such an IoT system would involve placing additional sensors, cameras and microphones, among others, and then connecting these to a network. Through this network, the data from these devices can be collected, cleaned and stored in a server, and then be used to create algorithms for predictive maintenance. Even if you don’t necessarily need or want predictive maintenance, such an infrastructure would be a great investment because the benefits go much further, like for data analyses or enhancing the work of monitoring teams.
Although the investment in the right infrastructure can be rather large, the potential benefits of predictive maintenance can be even larger. You can boost your overall equipment effectiveness. You can increase availability by reducing unplanned and planned stops. You can monitor the performance of your machines more closely and increase the overall machine performance quality. All while reducing the number of resources used and waste produced, bringing you closer to lean manufacturing.
Of course, an easily replaceable tool isn’t worth the investment of a predictive maintenance algorithm. So where do you start? By determining what kind of predictive maintenance your business could benefit from. If you are looking for guidance in this innovation journey, check out our AI services or get in touch!
