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Sensor technology transforming standard bearings for predictive maintenance

HCP Sense GmbH, a deep-tech company spun out of the Technical University of Darmstadt, was established in 2021 by Ansgar Thilmann, Dr. Tobias Schirra, and Georg Martin. The company's foundation is built upon research conducted at the Institute for Product Development and Machine Elements (PMD) at TU Darmstadt. A significant milestone was achieved in April 2025 when the company secured a seven-figure seed financing round led by BMH Beteiligungs-Managementgesellschaft Hessen mbH, with participation from several business angels. This funding is aimed at advancing their sensor solution to industrial series maturity. The firm operates in the industrial technology and machinery manufacturing sector, focusing on predictive maintenance and condition monitoring. Its core business revolves around a patented sensor technology that monitors the lubrication and measures forces within rolling bearings—a critical component in industrial machinery. This technology addresses a significant market need, as an estimated 80 percent of premature bearing failures are due to improper lubrication. The business model serves industrial clients, including DAX-listed corporations and medium-sized companies, by providing solutions that prevent equipment failures and optimize maintenance schedules. HCP Sense offers its technology both as a series application for integration into client products and as a "Measurement as a Service" for testing and prototype analysis. The company's main offering is a sensor that transforms a standard rolling bearing into a smart, data-providing device without requiring additional installation space or significant design modifications. This is achieved by utilizing the inherent electrical properties of the bearing itself to measure impedance, which correlates to lubrication conditions and load forces. The system provides real-time data on lubrication status, force, and can detect early signs of surface damage. This allows clients to move from traditional, often reactive, maintenance methods like vibration analysis to a predictive approach, thereby increasing machine availability and reducing costs. The data processing and visualization are customized for each application, ensuring an optimized analysis for the specific needs of the client. Keywords: predictive maintenance, condition monitoring, sensor technology, rolling bearings, lubrication analysis, force measurement, industrial IoT, machinery manufacturing, asset monitoring, deep-tech, TU Darmstadt spin-off, industrial sensors, equipment reliability, machine learning algorithms, non-invasive measurement, bearing failure prevention, IIoT bearing provider, asset health, impedance-based sensing, process optimization — this profile has been compiled from public sources. The founders haven't yet claimed it.