With artificial intelligence to maximum productivity
listen into your plants
With the tepcon machine learning solution, your production facilities always keep you up to date on how they are doing. Are all critical wear parts still in order and when should they ideally be replaced? Can all quality standards be maintained and is it possible to increase productivity? Your plants tell you all this. The systematic collection of valuable data with special sensors and the linking, processing and evaluation of this data in the IoT portal are the key to communication. AI in mechanical engineering represents a fundamental success factor and sets new standards in terms of efficiency, planning reliability and convenience.
Efficient utilisation of the lifetime of all critical wear parts, repairs according to plan, no unplanned machine downtimes and thus higher productivity overall - these are all the advantages of predictive maintenance. maintenance. By systematically recording and evaluating sensor data, you always have an overview of the wear status of critical machine parts and can reliably predict their ideal replacement time. Bottlenecks in spare parts procurement and manpower are a thing of the past. The service technician with the right spare part is always in the right place at the right time - because with tepcon at your side, you have the far-sighted planning for this in your hands! planning for this in your hand!
More performance in production: Machine learning monitors and ensures your quality standard. By analysing important factors influencing product quality, rework and reject rates are reduced. Optimised production processes are achieved by the user of the machine learning system being able to recognise at an early stage whether the machine load should be reduced or whether an increase in productivity is possible.
Both optical and acoustic sensors are used to record machine parameters and provide reliable measured values.
Special data acquisition systems process both analogue and digital sensor data and are significantly more powerful than conventional measurement solutions.
... and innovative software
The evaluation and display of the machine learning results as well as the control of the IPC are carried out via our IoT portal.
Storage and evaluation of sensor data in the cloud or on premise.
Data transmission from the measuring system (DAQ) to the IPC, as well as from the IPC to the cloud, is carried out using the AWS S3 standard.