Malfunctions of machines and assets, time-consuming maintenance and related production inefficiency pose major challenges to companies, making it all the more important that these production processes are able to function quickly, precisely, and undisturbed.
With the help of Predictive Maintenance, you are able to increase the availability and productivity of your equipment and systems and protect yourself from unnecessary costs for operation, maintenance, and repair. Based on experience rules, statistical methods or smart algorithms, failures or wear can be predicted, maintenance can be planned with pinpoint accuracy and processes can be automated.
The maintenance of your machines and assets is no longer carried out according to fixed maintenance intervals but based on actual usage data – precisely and only when truly necessary.
Thanks to Predictive Maintenance, damage can be predicted accurately and repaired before your machine breaks down.
With Predictive Maintenance, you not only protect yourself from expensive downtimes in your production, by analyzing the collected data you can also improve the performance of your equipment and achieve higher productivity.
A 5-minute production standstill in the automotive industry costs around 100,000 euros. Accordingly, a leading German car manufacturer has set itself the goal of ensuring smooth production processes with the help of condition monitoring and predictive maintenance. Unplanned downtimes should be kept to a minimum and possible production robot failures prevented at an early stage.
Based on a data lakehouse, Device Insight developed and integrated an innovative function for the early detection and prediction of certain anomalies. This involves monitoring parameters such as the motor temperature of the industrial robots, which can be predicted using statistical regression models. Thanks to the Databricks cloud service used, the statistical regression models can be easily scaled. Over 60,000 models are trained simultaneously. In addition, anomaly detection can be carried out across the entire machine fleet in just a few minutes. If deviations from the predicted engine temperature occur, the system notifies the employees on site. The average time required to carry out anomaly detection is 1.5 minutes.
We combine many years of IoT and data analytics expertise and support our customers in the implementation of reliable and high-performance Predictive Maintenance use cases – from data acquisition to the implementation of machine learning and AI.
Read about successful strategies proven effective in Manufacturing Analytics, learn about valuable technologies, and gain insights into modern solution approaches, including best-practice examples from our data engineering workbench.
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