Mittelstandspresse
25.10.2026
A Sensor Along for the Ride: SF2 Systems Makes Track-Wide Condition Changes Visible
How mobile condition monitoring flags anomalies along conveyor routes – without retrofitting every component.
Wien, 25.10.2026 (PresseBox) - SF2 Conveyor State Intelligence: A mobile sensor captures vibration and motion data along the route. SF2 condenses these signals into compact State Fingerprints, providing a clear, comparable view of health from individual rollers to the entire conveyor line.
A roller is running out of round. Then, the entire conveyor line comes to a standstill. It only takes a single worn roller. As long as it is only running slightly out of round, it is hardly noticeable during day-to-day operation. By the time the problem becomes audible or visible, a minor change in condition may already have led to a costly shutdown.
For multi-kilometer conveyor and transport systems, this is a structural challenge. Thousands of rollers, bearings, transfer points, and drives are in motion every day, but only a fraction are permanently monitored. The traditional solution – instrumenting every individual component –requires additionalsensors, complex wiring, high integration costs, and continuous maintenance.
The deep-tech company SF2 Systems presents a more cost-effective alternative: Turn the existing carrier into a mobile inspector.
Zero Retrofits: Mobile Sensing Replaces Fixed Hardware
SF2 pioneered this approach as part of a Thin[gk]athon organized by Infineon Technologies and ZEISS Digital Innovation, later implementing it on an instrumented carrier using an Infineon PSoC Edge E84 AI Kit.*
By equipping a shuttle, carrier, or test cart with sensors and edge processing, it monitors the underlying infrastructure during its routine run. A standard transport route doubles as an inspection route, radically altering the economic equation.
The Math: A 5,000-roller track would require at least 5,000 measuring points if monitored individually in a stationary manner. An instrumented carrier, on the other hand, passes all of them without the need to modify the conveyor system or interfere with the existing control system. A cloud connection is also not required for the analysis.
A Spike Isn't Necessarily a Defect: As speed, load, or curves create natural mechanical effects, a single sensor reading reveals little about an actual defect. SF2 therefore combines the synchronised sensor signals from a single pass into a compact State Fingerprint. This semantic fingerprint allows states to be compared: Does the segment still behave as it would during normal operation, or does its profile match a known defect pattern?
Technical Validation in Semiconductor Manufacturing: SF2 Systems verified this concept in an independent case study using sensor data from an Infineon cleanroom test conveyor. The case study comprises 418,938 processed measurement values and 72 derived state features along a 15-metret-long test section. Known defective roller sections were reliably recognised as recurring characteristic patterns in a fingerprint comparison.
The takeaway is not just the volume of data, but the underlying principle: A mobile sensor unit accurately detects and compares condition changes along a converyor route and makes them comparable without having to instrument a single individual roller.
From Proof-of-Value to Live Operations: Operators don't need to ask “How does Semantic Folding work?”, they need to know “What does this require on my line, and what do I get in return?” That is where the industrial pilot comes in.
Every conveyor has its own mechanical behaviour. Speed, load, route configuration, and roller and bearing types all vary. For this reason, the approach is tested on the operator’s specific conveyor line. In doing so, the parameters that are relevant for cost-effective operation are precisely determined:
• How accurately can anomalies be localized to specific track segments?
• How stable is detection at different speeds and load levels?
• How early are relevant wear trends detected in this specific setup?
Optical markers, as well as magnetometer and radar data, are among the methods envisaged for route localisation.
Getting Started: Lean Setup, Clear Decision What You Provide:
• A target conveyor segment
• An existing carrier, shuttle or test vehicle
• Existing sensor data or a few reference runs
What You Receive:
• Validation of whether recurring condition variations are detectable on your track
• A comparative analysis between normal runs and anomalous or known defect segments
• A solid technical foundation for next steps
• A clear decision matrix: stop, deepen, or roll out
The pilot is not intended to be a technical exercise, but as a targeted evaluation designed to guide technical and commercial decisions.
High-Impact Use Cases
This approach applies anywhere mobile carriers travel across extensive, high-load infrastructure:
• Intralogistics & Automated Storage: Roller conveyors, shuttles, and sorting systems
• Logistics & Distribution: Parcel hubs and sorting facilities
• Airports: Complex, long-distance baggage handling systems
• Automotive & Manufacturing: Workpiece carriers, skillets, and transfer lines
• Semiconductor Fab: Wafer transport and cleanroom conveyors
• Pharma & Food: Sensitive or hygiene-critical transport systems
The common denominator: High component counts, long distances, strict uptime demands, and no viable business case for instrumenting every point.
A New Service Model for OEMs
For conveyor manufacturers and system integrators, this approach can give rise to a new service business. Condition monitoring no longer requires selling static hardware for every single component. Selected carriers can be fitted with sensors, their status regularly recorded, and the same SF2 software logic deployed across various customer installations.
For OEMs, this opens up the prospect of an additional digital service for both new and retrofits, delivering actionable condition intelligence via mobile measurement nodes and local State Intelligence.
Show Us Your Track
“Why instrument thousands of components individually when a single sensor can pass by them every day?” - Christoph Gretzmacher, Business Development at SF2 Systems
SF2 is actively seeking operators, plant manufacturers, and system integrators to pilot this solution on live production routes.
The onboarding process is straightforward:
Select track Reference runs Compare states Test in real-world operation Decide.
Does a carrier, shuttle, or container already travel the same route in your facility every day?
If so, thenlet's take look at your track.
SF2 Edge Challenge
SF2 – From Signals to State.
*) This reference describes the development and testing context and does not constitute a partnership or a manufacturer’s recommendation.
SF2 Systems GmbH
SF2 Systems is a Vienna-based deep-tech company specializing in software-based condition analysis of complex machines, industrial assets, and processes across industry, energy, and critical infrastructure. Vendor- and sensor-agnostic, the technology combines existing operational data into comparable state fingerprints. This makes drift and gradual degradation visible early – reproducibly, transparently, without black-box models, and without requiring large labeled failure datasets.
Designed to complement existing condition-monitoring and automation systems, SF2 is built for brownfield and OT environments. It runs locally at the edge or on-premises with zero cloud dependency, keeping sensitive operational data strictly in-house. This enables early anomaly detection, reduces unplanned downtime, and makes maintenance far more predictable.
Additionally, SF2 is exploring applications for its core technology in biotechnology and life sciences.
Ansprechpartner
Christoph Gretzmacher
Zuständigkeitsbereich: Business Development
Datei-Anlagen:
(2 MB)
1635233.attachment
One mobile sensor unit can inspect thousands of rollers during normal operation — without retrofitting the conveyor, adding fixed sensors to every component, or relying on the cloud.
SF2 combines synchronized sensor signals from each pass into compact State Fingerprints, making condition changes along the route comparable and helping identify recurring anomalous patterns.
Turn transport into inspection.
Turn signals into state.
- Mehr Infos zu dieser Meldung unter www.pressebox.de
- zurück zur Übersicht
