In the past, a production plant had to be down before it could be repaired or faulty parts replaced. Today, a malfunction can be predicted even before it occurs. Predictive maintenance is the new buzzword that can save enormous costs and open up new business models.
Predictive maintenance is the retrieval of maintenance information based on real machine and production data with the aim of proactively maintaining machines and systems before they come to a standstill. Those who manage to predict impending machine failures and prevent them accordingly save considerable downtime costs. But how can predictive maintenance be implemented?
1. Proactive maintenance through additional systems on the machine
There are several options. On the one hand, predictive maintenance can be implemented through integrated machine monitoring. For this purpose, machines and industrial plants are equipped with sufficient sensors that measure vibration, temperature or humidity, for example, which later allow remote monitoring during operation. Defective components, which may soon lead to the system coming to a standstill, are thus identified independently of the usual maintenance times and can be replaced before any actual damage occurs.
2. Condition monitoring using the digital twin
Another way is to map the real machine behavior using a digital twin. The only difference: the twin – i.e. the virtual counterpart of the system – runs on a normal PC. Ideally, the model is connected to the real machine control system and runs a defined number of cycles in advance. There is a high probability that failures on the virtual machine will also occur on the real machine after the defined time has elapsed. Depending on the complexity of the system and the level of downtime costs, the look-ahead period should be selected accordingly in order to keep downtimes as short as possible.
But how do you get the digital twin? With the iPhysics simulation software, for example, machineering has created a platform with which a simulation model can be created quickly and easily from CAD data. Connected to real control systems, the virtual system is brought to life and behaves in the same way as the real system. In combination with manufacturer information on material wear etc., a failure due to material fatigue can be predicted quite reliably.
Predictive maintenance saves a lot of money
The main advantage of predictive maintenance can be reduced to one central keyword: Cost-effectiveness. Regardless of which systems are involved, the savings potential is huge. The US rail transportation company Union Pacific says it saves around 100 million dollars a year thanks to predictive maintenance. The maintenance of machines and systems is entering a new era. Even if this technology has only been implemented in a few companies so far, there is a clear trend in this direction.
Interested in discovering more about iPhysics? Request a brochure now!
Links to the website
General link:
Learn more about iPhysics and our virtual commissioning solutions on our website: www.machineering.com.
Directly to the product page:
Discover iPhysics – your solution for cloud-based simulation: www.machineering.com/en/products/iphysics
Contact page:
Do you have any questions? Contact us for more information: www.machineering.com/en/contact
Overview of VIBN:
Find out more about virtual commissioning and its benefits here www.machineering.com/en/virtual-commissioning
