The maintenance of machinery & production equipment is possible in several ways. In the age of digitalization and Industry 4.0, an intelligent solution has been added. Large amounts of data are collected in real time from sensor data of the machines and stored in Data Lakes. The data is there, why not use it for predictive maintenance? Why do you wait until expensive machines are damaged? Why do you replace spare parts even though they would surely run longer? With the traditional methods, you are running behind the situation. In our globalized world, no company can afford to be left behind in competition. Therefore, it is essential to be open to new technologies and approaches.
This is where customer-focused solutions from IIoT platform providers like Quantis come in handy.
When switching to predictive maintenance of machines or entire plants, close cooperation with the maintenance manager is essential, because he knows his plants better than anyone else.
The benefits of predictive maintenance are obvious.
Digitized data from machines and plants help to detect and eliminate leaks. Leakages are one reason for excessive energy consumption. In view of the significant increase in energy prices, resource-saving production should be strived for.
The service life of spare parts is increased. By mapping wear parameters, such as temperature, vibration or pressure, and the specified service life, components such as valves or pumps can be monitored. The system alerts the Maintenance Manager in case of deviating normal pattern behavior. In this way, the corresponding spare part can be checked and replaced in good time. This significantly increases the utilization of spare parts. It also prevents expensive machine downtime caused by broken spare parts.
In obsolete plants and machines, it is not always possible to obtain data from relevant spare parts. Here, too, process know-how can generate a cost-effective solution, for example by recording the effective operating hours of the spare parts. It is important here to define and map the weightings of the individual load phases of the spare parts. Machine learning is used to calculate wear data that indicates when the spare parts are likely to start deteriorating.
Another advantage for the maintenance manager is the elimination of a visual inspection in the machine shop, since all spare parts are mapped visually and easily understandable in dashboards on a PC or tablet. This saves valuable working time.
In complex interlinked plants, faults from upstream or downstream machines can filter through to the master machine. The result is machine or plant downtime. Using AI, the Timestealer algorithm detects deteriorations on machines and categorizes and prioritizes them. When deterioration creeps in, a notification is sent and warns the maintenance manager in time so that improving measures can be taken.
In addition, the algorithm indicates whether measures taken have led to improvements in production.
Digitizing entire bottling lines is like going on a journey. The switch to predictive maintenance is a process in which the maintenance manager is constantly coming up with new ideas for data collection and digitization of components. This requires a certain pioneering spirit and hands-on mentality. The IIoT provider then implements the specified goals. In this way, a path is created together, little by little, to digitize production and perform predictive maintenance.
Credits Unsplash Foto Tim Mossholder