Predictive maintenance: anticipating problems before they occur

“The trick is to optimize system availability continuously,” says Dr. Maximilian Beinhofer, Head of Cognitive Systems Development at TGW. Condition monitoring and predictive maintenance provide a good solution. 

Predictive maintenance uses sensors to monitor the condition of components and software simulation to see whether a problem is coming up. Using smart algorithms TGW take data that have already been provided by sensors and link and merge these data in an intelligent way that allows to make very precise statements about the condition or wear of components. It saves expenses because no additional sensors must be installed. To give an example: TGW’s Rovolution picking robot measures the status of the vacuum of the gripping device. If there is a pressure loss, due to the dust load of the environment, for example, this is immediately spotted and action can be taken.

Of course, there is always an option of installing additional sensors. Depending on the size of the system, the number of sensors required may range between just a few and several hundred. For this reason, a cost-benefit analysis should be run on beforehand. 

The biggest challenge of predictive maintenance is to create maximum leverage with minimum effort. Another challenge is to use the networks of the system in such a way that the data required for the predictive maintenance software can be transmitted. And the feedback loops are the third challenge. As a manufacturer, one has to develop intelligent methods so that feedback is both outputs immediately and suitable for automated evaluation. 

Discover more on predictive maintenance here – www.tgw-group.com/en/news/press-releases/2021/predictive-maintenance-anticipating-problems-before-they-occur