VIPFLUID aims to collect operational data from wastewater pumps using appropriate sensor technology, utilizing the data for machine learning purposes. Initially, adaptive sensor technology and an intelligent sensor hub will collect and preprocess the pump data locally before sending a compressed data stream to local computing resources (fog). Synthetic data generated through machine learning (ML) will then enable local, adaptive, predictive models that are resource-efficient.
The software solutions developed are based on generative neural networks and federated learning. They promote the economical and environmentally friendly use of machine learning in predictive maintenance. These solutions enable proactive maintenance and minimize reactive actions. This avoids costly, system-critical failures and significantly improves process reliability. Reliable predictive results allow for a significant reduction in costs of maintenance. This technology reduces CO₂ emissions, thereby supporting the achievement of climate and environmental protection goals.
Funding period: May 1, 2023 – April 30, 2026
IInternationally, the calibration of seismometers is a key area of focus. In addition to having a very low frequency range, these test devices differ from standard vibration sensors primarily in terms of their geometric shape and significantly greater mass. Furthermore, as these sensors typically measure vibrations in three spatial directions, they must also be calibrated in three spatial directions.
The transition from 50 mHz to a few millihertz is no longer feasible with current approaches and resources. This project aims to develop new methods for designing calibration vibration exciters that can precisely excite low-frequency, heavy sensors in the horizontal direction. Two main tasks require particular attention:
- Development of a long-stroke system with extremely precise straight-line motion for installation in a vibration exciter capable of excitation starting at a few millihertz.
- Development of a long-stroke system that can absorb tilting moments arising during the high-frequency horizontal excitation of heavy test devices, reducing tilting motion to a minimum.
- Development of a design concept that prevents tilting motions during high-frequency horizontal excitation of heavy test devices.
Funding period: June 1, 2023 – November 30, 2025
The goal of this project is to develop a combined test system that cost-effectively integrates the key aspects of "precise sensor excitation" and "fast, parallel sensor testing," thereby making it suitable for a wide range of MEMS sensors. New measurement technology is essential to test the next generations of MEMS sensors with sufficient accuracy and to respond flexibly to growing testing requirements.
The measurement hardware must perform many parallel tasks. It must be a modular measurement card (operating autonomously) with sufficient hardware resources to support and test a variable number of test devices (or groups of test objects). The semiconductor test systems currently available on the market are too large for effective use in sensor development and too expensive for use in final measurement.
An optimized test system for high-precision and cost-effective testing of MEMS sensors can fill an economic gap in the test market. The high integration of many measurement functions on a single measurement card meets the requirements of the MEMS sensor segment.
Funding period: August 1, 2023 – July 31, 2026
SRIMS is a major German-Czech project being carried out in collaboration with Statotest, the Faculty of Civil Engineering at the Czech Technical University in Prague (CTU), and partners in Saxony — the Fraunhofer IIS and Spektra Schwingungstechnik und Akustik GmbH Dresden. The goal of this project is to develop a monitoring system for rail transport designed to significantly improve the safety and efficiency of the rail network.
The project focuses on the safety and stability of railway bridges and the associated infrastructure, including rails, superstructure, substructure and switches, which are essential for reliable rail operations. The research centers on the assessment of critical infrastructure, the development and mapping of sensor systems for data collection, as well as numerical modeling for risk analysis and condition prediction. In addition, an expert platform with cloud connectivity is being developed to efficiently analyze the collected data. Key outcomes include a validated monitoring system that combines specialized sensors with a cloud-based expert system, as well as a test environment for system validation and the development of intelligent algorithms.
Funding period: January 1, 2024 – December 31, 2025
In SysDamp, the vibration behavior of wind turbines is studied in great detail. Various experimental and numerical methods are employed with the aim of determining damping parameters from field measurements, which are then incorporated into the design process for new turbines. The goal is to develop a strategy for future wind turbine prototypes.
- Generation of measurement data using sensors already installed on a wind turbine at the WiValdi research wind farm to determine the modal damping of the entire system.
- Development of a multi-shaker inertial excitation system for the targeted excitation of vibration-relevant modes from the nacelle area of the wind turbine.
- Generation of measurement data using existing sensors on an operational wind turbine under specifically controlled operating modes, some of which are also off-design.
- Development of methods to determine the damping values of the tower and blade modes of the entire wind turbine from measurement data using accelerated, fully automated approximation algorithms in the time and frequency domains, with the goal of tracking more than 15 modes.
- Model and result validation of the aeroelastic simulation of the wind turbine is performed using the damping parameters identified from the field test; component (section) loads in various load cases are compared with the test data, and a comparison is made with conventional models.
- Automated calibration of damping between measurement and simulation to improve model quality and prediction accuracy.
- Determination of the influence of uncertain damping parameters using sensitivity analysis.
Funding period: January 1, 2024 – December 31, 2026
Learn more about the project in this post.
After thoroughly analyzing the current system landscape, the requirements are precisely defined. Based on these requirements, a technical and functional concept is developed to ensure the integration of existing systems. Next, the SPEKTRA HUB is implemented in phases, beginning with core areas such as data management, user administration and document management. Simultaneously, an intuitive user interface is developed to simplify operation and optimize workflows. After the technical implementation is complete, the system will be rolled out across the entire company. A key focus is employee training to ensure seamless integration and high acceptance. After the rollout, the SPEKTRA HUB's operation is regularly evaluated to identify and implement potential improvements early on.
Funding period: February 3, 2025 – February 28, 2026
Thanks to funding from the EU, as well as from federal and national sources, we are able to invest in targeted research and development of cutting-edge technologies. This allows us to drive innovation, explore new areas of application and continuously refine our solutions.
This gives us greater flexibility and the ability to efficiently implement even the most challenging projects. Our customers benefit from forward-looking technologies, optimized processes and cost-effective, cutting-edge solutions.