SF2 Edge State Intelligence: Instead of transmitting continuous streams of raw data to the cloud, SF2 condenses multivariate sensor data directly at the edge into compact State Fingerprints. This creates highly actionable state information while drastically reducing network traffic.
Industrial facilities are generating more sensor data than ever before. However, evaluating whether a machine is operating within its normal range doesn't necessarily require a constant transmission of raw data. SF2 Systems takes a more efficient approach: state calculation happens exactly where the data is generated – at the edge. Instead of pushing full raw data streams, only relevant state information is forwarded.
When Sensors Become a Transport Problem
Vibration, temperature, pressure, power consumption, revolutions per minute, flow rate, IMU, and process data: modern machines and systems deliver dozens or even hundreds of metrics simultaneously. While this depth of data is technically invaluable, it raises a critical architectural question: which of this data actually needs to leave the machine?
The traditional route is usually: capture, transmit, store, and centrally analyze. This works perfectly well for individual machines, but quickly hits its limits with hundreds or thousands of assets, mobile systems, remote locations, or high-frequency sensors. In these scenarios, bandwidth, cloud costs, latency, and data sovereignty become critical bottlenecks. The solution is not to transmit more data even faster, but to extract reliable state information from raw data directly at the edge.
From Countless Metrics to a Single State Fingerprint
Using Semantic Folding Sensor Fusion, SF2 Systems processes multivariate sensor and operational data entirely locally. The collective behavior of these signals is translated into compact, sparse binary State Fingerprints.
Such a fingerprint doesn't just replace a single metric; it represents the entire state of a system within its specific context. Similar states produce similar fingerprints. If the interplay of signals changes, drift, state transitions, or previously unknown states become immediately comparable. This enables an edge device to locally determine whether an asset is behaving according to a known reference state or if its overall behavior is shifting.
In simple terms, the architectural shift looks like this:
* Traditional: Sensors → Raw Data Stream → Network/Cloud → Central Analysis
* With Local State Intelligence: Sensors → Local State Analysis → State Fingerprint / Similarity Information → Higher-Level System
The raw data is not lost in this process. It can still be stored locally or transmitted on demand. SF2 does not act as a conventional raw data compression algorithm, but establishes an additional, highly condensed layer of information.
The Math: How 99.9% Data Reduction is Achieved
The magnitude of this efficiency is easy to calculate. Consider a purely illustrative example: an asset has 50 sensor channels delivering 100 measurements per second.
This results in:
-> 5,000 metrics per second
-> 300,000 metrics per minute
-> 432 million metrics per day
With 32-bit values alone, this generates around 1.7 gigabytes of raw data daily – even before accounting for timestamps, protocols, and transmission overhead.
If we assume a simple architectural shift where, instead of a continuous raw data stream, a 1-kilobyte state update is transmitted just once per minute, the total drops to a mere 1.44 megabytes per day. Compared to 1.7 gigabytes of raw data, this represents a reduction of more than 99.9 percent.
The crucial difference from simple downsampling or basic averaging is that a State Fingerprint contains more than just a reduced selection of individual metrics. It captures the interaction of all channels as a holistic system state. This reveals changes that would go entirely undetected by isolated averages or traditional threshold monitoring.
The actual data reduction naturally depends on the number of sensors, sampling rate, fingerprint size, update frequency, and the required scope of additional telemetry. The 99.9 percent figure is not a blanket SF2 performance guarantee, but a realistic magnitude that can be precisely measured for any specific edge infrastructure.
52 Sensor Channels: A Real-World Industrial Use Case
The fact that SF2 can process complex, multivariate state spaces with high precision is demonstrated by a documented pump use case. In this instance, 52 sensor channels were evaluated simultaneously. The dataset comprised over 220,000 entries with only seven documented failures.
In one analyzed sequence, a relevant shift in the system's overall behavior became visible roughly four days before a documented mechanical pump failure. These four days don't represent a universal prediction window, but rather the empirical result of this specific sequence.
For edge architectures, the core takeaway is this: countless individual metrics can be transformed into a compact, comparable representation of state. The question is no longer just "What values were measured?" but rather "How is the system behaving as a whole?"
Designed for Distributed and Hard-to-Reach Assets
This approach unlocks its full potential wherever continuous raw data transmission is expensive, inefficient, or simply unnecessary. This applies particularly to wind and energy turbines, water infrastructure, offshore systems, mobile machinery, production lines, transportation networks, or large fleets.
An asset essentially functions as a local State Node. Components generate their own state information, which flows into a subsystem state. Subsystems, in turn, can become part of a higher-level facility or system representation.
This creates a highly efficient information hierarchy:
Component → Subsystem → Facility → Site → Fleet
Instead of redundantly transporting all raw data at every level, only the relevant state information is passed up the chain.
Edge Over Cloud – But Not Against the Cloud
This approach is not opposed to centralized platforms. Cloud systems remain essential for historical analysis, fleet management, reporting, engineering, and optimization. The decisive architectural question is rather: which calculations absolutely must take place centrally, and what information can be deterministically generated exactly where the data is created?
"Our goal isn't to move as much data away from a machine as possible," says Christoph Gretzmacher, Business Development at SF2 Systems. "What matters is what information an operator actually needs. If you can generate a reliable snapshot of the system's state right at the edge, that insight is what your architecture should be built around."
Physical AI Needs Internal Perception, Too
With the rise of Physical AI, this division of tasks is becoming increasingly relevant. Modern edge platforms reserve substantial computing power for cameras, Vision AI, and autonomous decision-making processes.
These systems primarily answer questions about the environment:
- What does the machine see?
- What is happening in front of it?
- What action should it take?
Alongside this exists a second, equally critical dimension: the operational behavior of the machine itself.
SF2 refers to this layer as Physical State Intelligence. The runtime evaluation is designed for local CPU classes and does not require mandatory GPU/CUDA infrastructure. The specific performance is measured individually on the respective target hardware. This allows Perception (environmental awareness) and Physical State Intelligence (internal state analysis) to be unified as distinct but complementary tasks on the same edge architecture.
The SF2 Edge Challenge: Put Your Own Data to the Test
How substantial is the actual data reduction in your specific industrial application? This is exactly what SF2 is determining in collaboration with operators, OEMs, sensor manufacturers, and edge platform providers.
The core question is: How massive is your current raw data stream, and how significantly could your data transmission volumes be reduced if state calculation happens locally at the edge?
This approach unlocks the greatest value for use cases involving massive sensor counts, high sampling rates, bandwidth-constrained remote assets, or large-scale fleets.
You can run a preliminary baseline test using just historical time-series data, entirely offline and without touching your active PLC environment. To make this seamless, the SF2 Suite SE is available as a permanently free tool for local, cross-platform analysis (Windows, macOS, Linux).
Benchmark your own asset data today.
www.sf2systems.com
edge_challenge@sf2systems.com
SF2 – From Signals to State.
Industrial facilities are generating more sensor data than ever before. However, evaluating whether a machine is operating within its normal range doesn't necessarily require a constant transmission of raw data. SF2 Systems takes a more efficient approach: state calculation happens exactly where the data is generated – at the edge. Instead of pushing full raw data streams, only relevant state information is forwarded.
When Sensors Become a Transport Problem
Vibration, temperature, pressure, power consumption, revolutions per minute, flow rate, IMU, and process data: modern machines and systems deliver dozens or even hundreds of metrics simultaneously. While this depth of data is technically invaluable, it raises a critical architectural question: which of this data actually needs to leave the machine?
The traditional route is usually: capture, transmit, store, and centrally analyze. This works perfectly well for individual machines, but quickly hits its limits with hundreds or thousands of assets, mobile systems, remote locations, or high-frequency sensors. In these scenarios, bandwidth, cloud costs, latency, and data sovereignty become critical bottlenecks. The solution is not to transmit more data even faster, but to extract reliable state information from raw data directly at the edge.
From Countless Metrics to a Single State Fingerprint
Using Semantic Folding Sensor Fusion, SF2 Systems processes multivariate sensor and operational data entirely locally. The collective behavior of these signals is translated into compact, sparse binary State Fingerprints.
Such a fingerprint doesn't just replace a single metric; it represents the entire state of a system within its specific context. Similar states produce similar fingerprints. If the interplay of signals changes, drift, state transitions, or previously unknown states become immediately comparable. This enables an edge device to locally determine whether an asset is behaving according to a known reference state or if its overall behavior is shifting.
In simple terms, the architectural shift looks like this:
* Traditional: Sensors → Raw Data Stream → Network/Cloud → Central Analysis
* With Local State Intelligence: Sensors → Local State Analysis → State Fingerprint / Similarity Information → Higher-Level System
The raw data is not lost in this process. It can still be stored locally or transmitted on demand. SF2 does not act as a conventional raw data compression algorithm, but establishes an additional, highly condensed layer of information.
The Math: How 99.9% Data Reduction is Achieved
The magnitude of this efficiency is easy to calculate. Consider a purely illustrative example: an asset has 50 sensor channels delivering 100 measurements per second.
This results in:
-> 5,000 metrics per second
-> 300,000 metrics per minute
-> 432 million metrics per day
With 32-bit values alone, this generates around 1.7 gigabytes of raw data daily – even before accounting for timestamps, protocols, and transmission overhead.
If we assume a simple architectural shift where, instead of a continuous raw data stream, a 1-kilobyte state update is transmitted just once per minute, the total drops to a mere 1.44 megabytes per day. Compared to 1.7 gigabytes of raw data, this represents a reduction of more than 99.9 percent.
The crucial difference from simple downsampling or basic averaging is that a State Fingerprint contains more than just a reduced selection of individual metrics. It captures the interaction of all channels as a holistic system state. This reveals changes that would go entirely undetected by isolated averages or traditional threshold monitoring.
The actual data reduction naturally depends on the number of sensors, sampling rate, fingerprint size, update frequency, and the required scope of additional telemetry. The 99.9 percent figure is not a blanket SF2 performance guarantee, but a realistic magnitude that can be precisely measured for any specific edge infrastructure.
52 Sensor Channels: A Real-World Industrial Use Case
The fact that SF2 can process complex, multivariate state spaces with high precision is demonstrated by a documented pump use case. In this instance, 52 sensor channels were evaluated simultaneously. The dataset comprised over 220,000 entries with only seven documented failures.
In one analyzed sequence, a relevant shift in the system's overall behavior became visible roughly four days before a documented mechanical pump failure. These four days don't represent a universal prediction window, but rather the empirical result of this specific sequence.
For edge architectures, the core takeaway is this: countless individual metrics can be transformed into a compact, comparable representation of state. The question is no longer just "What values were measured?" but rather "How is the system behaving as a whole?"
Designed for Distributed and Hard-to-Reach Assets
This approach unlocks its full potential wherever continuous raw data transmission is expensive, inefficient, or simply unnecessary. This applies particularly to wind and energy turbines, water infrastructure, offshore systems, mobile machinery, production lines, transportation networks, or large fleets.
An asset essentially functions as a local State Node. Components generate their own state information, which flows into a subsystem state. Subsystems, in turn, can become part of a higher-level facility or system representation.
This creates a highly efficient information hierarchy:
Component → Subsystem → Facility → Site → Fleet
Instead of redundantly transporting all raw data at every level, only the relevant state information is passed up the chain.
Edge Over Cloud – But Not Against the Cloud
This approach is not opposed to centralized platforms. Cloud systems remain essential for historical analysis, fleet management, reporting, engineering, and optimization. The decisive architectural question is rather: which calculations absolutely must take place centrally, and what information can be deterministically generated exactly where the data is created?
"Our goal isn't to move as much data away from a machine as possible," says Christoph Gretzmacher, Business Development at SF2 Systems. "What matters is what information an operator actually needs. If you can generate a reliable snapshot of the system's state right at the edge, that insight is what your architecture should be built around."
Physical AI Needs Internal Perception, Too
With the rise of Physical AI, this division of tasks is becoming increasingly relevant. Modern edge platforms reserve substantial computing power for cameras, Vision AI, and autonomous decision-making processes.
These systems primarily answer questions about the environment:
- What does the machine see?
- What is happening in front of it?
- What action should it take?
Alongside this exists a second, equally critical dimension: the operational behavior of the machine itself.
SF2 refers to this layer as Physical State Intelligence. The runtime evaluation is designed for local CPU classes and does not require mandatory GPU/CUDA infrastructure. The specific performance is measured individually on the respective target hardware. This allows Perception (environmental awareness) and Physical State Intelligence (internal state analysis) to be unified as distinct but complementary tasks on the same edge architecture.
The SF2 Edge Challenge: Put Your Own Data to the Test
How substantial is the actual data reduction in your specific industrial application? This is exactly what SF2 is determining in collaboration with operators, OEMs, sensor manufacturers, and edge platform providers.
The core question is: How massive is your current raw data stream, and how significantly could your data transmission volumes be reduced if state calculation happens locally at the edge?
This approach unlocks the greatest value for use cases involving massive sensor counts, high sampling rates, bandwidth-constrained remote assets, or large-scale fleets.
You can run a preliminary baseline test using just historical time-series data, entirely offline and without touching your active PLC environment. To make this seamless, the SF2 Suite SE is available as a permanently free tool for local, cross-platform analysis (Windows, macOS, Linux).
Benchmark your own asset data today.
www.sf2systems.com
edge_challenge@sf2systems.com
SF2 – From Signals to State.
