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Ultra-low Power Retrofit IoT Sensor Platform for Predictive Maintenance in Paper Manufacturing

The aim of this project is to automate the maintenance process and to enable predictive maintenance. For this purpose, wireless sensor units, which supply energy from the machine heat and send sensor data to a central computer if required, are retrofitted. The combination of a retrofittable, energy-autonomous sensor platform and AI makes it possible to make production processes in the industry smart, efficient and reliable. Especially the paper-producing industry faces great challenges in meeting quality demands and efficient production without unexpected downtime. The production of paper is very complex. At temperatures of up to 80 °C and a humidity of up to 100%, paper is produced in plants, that are several hundred meters long and consist of several hundred rolling bearings, drives and drive rollers. Each of these components can lead to a standstill and must therefore be regularly maintained. The components to be monitored, such as bearings and drives, are also found in other manufacturing industries such as the pharmaceutical and food industry. Nevertheless, the need for predictive maintenance in paper production is much higher, because these products, e.g. special papers for the furniture industry, play an important role and downtime leads to delivery delays and high follow-up costs.

The uniqueness of this project’s approach is the combination of wireless and energy-efficient hardware with intelligent algorithms to efficiently and reliably detect anomalies. The change detection on the microcontroller automatically learns at the gateway and detects all changes, these are transmitted by radio and thus the entire system is even more energy-efficient. Efficient and scalable predictive maintenance and OEE analysis can only be achieved in combination with energy-saving hardware and intelligent software.

In terms of data processing, the key idea and innovation is that the rather complex and compute-intensive algorithm for automatically detecting bearing state changes is divided into different parts. The various parts of the AI software run on different devices and have features that exactly match the corresponding hardware specifications. In this case, the endiio Retrofit Box and the endiio Gateway. The Retrofit Box algorithm reduces high-frequency sensor data (acceleration, vibration, gyroscope and magnetic field) to a few, particularly relevant features. The features must be condensed in such a way that the power consumption for the wireless transmission is minimal and at the same time they contain enough information for the higher-level algorithm to be able to detect changes in the state of the bearings. The algorithm on the gateway uses the previously extracted features from multiple states and automatically detects changes in state and behavior. The algorithm considers different aspects: time-dynamic behavior of each individual sensor data (i.e. acceleration) and their relation with each other, the state of each bearing and their relation with each other.



“In the project, a retrofittable and wireless IoT sensor platform with extremely low power consumption in combination with embedded AI algorithms for predictive maintenance was implemented. For the first time it was possible to monitor rolling bearings in paper production at ambient temperatures of up to 115 °C. This makes it possible to identify and understand failures weeks in advance and reduce costly down time.”

Dr. Tolgay Ungan, Endiio


“With the continuous data analysis of the sensor information and trend changes, it is, for the first time, possible to recognize the condition of rolling bearings that are difficult or impossible to access over a longer period of time. For Kämmerer this results in savings in the areas of avoided breakdowns of the paper machines as well as utilization of the service life of the bearing units and better planning of maintenance activities.”

Mr. Pieper, Kämmerer


The project has shown that artificial intelligence with the right algorithms is possible even with little computing power. This opens up new markets that previously had little or no AI and thus the creation of new savings potential is possible".

Dr. Felix Sawo, Knowtion


Project type



Project budget

(IoT4industry funding): 45 000 €


Project end date

(estimated): October 19,  2019


Partners involved

Logo / Website Name Type Country Region
Knowtion UG SME Germany DE1-Baden-Würtemberg Kämmerer Spezialpapiere GmbH Large Enterprise Germany DE9-Niedersachsen


Vertical sector addressed

  • Paper production

Industrial application addressed

  • Predictive maintenance
  • Monitoring & control

Technologies involved

  • IoT & wearables
  • Big Data & AI


Contact Project Coordinator

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