PREDICTIVE MAINTENANCE FOR INDUSTRY
Predictive maintenance is a topic with enormous potential. We have addressed this to help you reduce unplanned machine downtime and thus increase the longevity of your machines.
OUR SOLUTION FOR PREDICTIVE MAINTENANCE
We have developed a cloud-based software solution in the area of predictive maintenance, which evaluates and visualizes sensor data from machines. Our tool for this is our specially developed algorithm, which combines the advantages of statistical methods with those of neural networks.
Our software solution allows you to monitor your machines comfortably and easily in your browser. You can see all important information at a glance and easily add or remove machines and sensors.
Predictive maintenance has never been so easy!
Predictive maintenance is one of the topics when it comes to Industry 4.0. However, there are four key challenges that no one talks about and which explain why predictive maintenance is not yet established in the industry. We have developed a solution that allows us to fix these problems once and for all.
Need for historical data
Previous PdM approaches require an enormously large historical training data set that contains previous errors in machine behavior. The algorithm learns these anomalies and can predict them in the future. Our method takes a completely different approach: Our algorithm is trained on the normal state of the machine.
This not only makes us independent of large historical data sets, but also allows us to predict unknown errors.
The term PdM is often mistakenly used for the simple analysis of sensor data. However, such an analysis cannot predict the future state of the machine. Our method changes this. Based on probabilistic models, it can include random variables and probability distributions and integrate any number of data sources. Thus, we can make actual and robust predictions.
Setting limit values
A serious problem of current approaches is the necessity of setting limit values.
However, ai-omatic has developed a method that can detect anomalies even without predefined limit values by means of a complex mathematical model. By integrating this automatic limit detection, our software can be used completely independent of expert knowledge.
To be able to analyze your condition data, we need access to your data. To ensure that this is transmitted to us securely, we have developed our own API. The software runs in the Azure Cloud. In selected cases, a more elaborate and cost-intensive on-premises solution is also possible. Our method complies with German standards and is also accepted by large companies in Germany.
"Current predictive maintenance solutions do not do justice to the term predictive maintenance. With ai-omatic solutions we change this"
Lena Weirauch, CEO & Co-Founder