In critical infrastructures, risk rarely begins at the moment a temperature threshold is exceeded. It usually appears earlier—when the thermal curve starts drifting from its natural trend, accelerates unexpectedly, or develops a small local imbalance. Distributed Thermal Gradient Sensing (DTGS) focuses precisely on this behavior. It transforms a fiber optic line from a simple temperature channel into a continuous analytical layer that interprets how heat evolves over time.
DTGS Technology
A New Standard for Early Warning Through Thermal Trend Analysis in Fiber Optic Infrastructure
What Is DTGS?
DTGS analyzes temperature data along a fiber not as individual point measurements but as a time-dependent and spatial trend. It evaluates how temperature changes, how fast it rises, whether the slope deviates from the natural curve, and how the thermal pattern behaves across the entire line.
This allows DTGS to deliver much earlier warnings than threshold-based systems.
How DTGS Works (Clear Technical Summary)
As a laser pulse moves through the fiber, Raman scattering occurs. The returned backscatter signal contains temperature-sensitive spectral components. DTGS interprets this data not only as “degree values” but also through engineering metrics such as trend, rate, deviation, acceleration, and gradient continuity.
In essence, DTGS doesn’t just tell you “the temperature is 36°C.”
It tells you: “This region’s thermal trend is drifting from normal and the risk is increasing.”
Core Advantages of DTGS
Early Warning Without Waiting for a Threshold
Even if the temperature is still within normal range, an abnormal change in slope or speed is flagged immediately. This brings operators valuable time—sometimes hours.
Trend-Based Risk Prediction
DTGS evaluates the evolution of temperature, not just its value.
Any disruption to the natural trend is highlighted early.
Detection of Localized Irregularities
Because the entire fiber acts as a distributed sensor, DTGS can reveal:
- connection fatigue
- insulation degradation
- localized thermal stress
- friction-induced micro-heating
Lower Alarm Noise
Natural variations are filtered out. Only meaningful deviations reach the operator, avoiding alert fatigue.
More Than Measurement — Real Interpretation
By combining time and spatial analysis, DTGS delivers engineering-level insight that helps teams act proactively instead of reactively.
Real-World Technical Scenarios
✔ Thermal Runaway Precursors in Lithium-Ion Storage
Before runaway occurs, thermal instability and curve acceleration appear. DTGS detects these early distortions even when everything still looks normal.
✔ Localized Thermal Irregularities in Solar DC Cables
Loose connectors or insulation fatigue change the thermal profile. DTGS identifies the deviation early enough for preventive maintenance.
✔ Friction Trends in Conveyor Systems
Subtle friction changes reveal themselves before temperature increases significantly. DTGS captures these micro-patterns.
✔ Insulation Weakening in Power Transmission Lines
Insulation problems distort the thermal trend before any heat builds up. DTGS picks up these deviations instantly.
✔ Pre-Leak Thermal Disturbances in Petrochemical Pipelines
Before a leak forms, the thermal equilibrium shifts locally. DTGS highlights this early disturbance.
Role of DTGS in the FOTAS Platform
Within the FOTAS platform, DTGS is not just another measurement function; it is an analytical layer that interprets thermal behavior. It processes temperature data over time, evaluates deviations from the natural trend, and reveals accelerating patterns that signal developing risk. This transforms raw thermal information into actionable engineering insight.
Conclusion: Fiber Optics Are No Longer Just Data Carriers — They Read Thermal Logic
Every major failure begins with a subtle signal. DTGS is the technology that captures that signal. With DTGS, a fiber optic line becomes more than a medium that measures temperature; it becomes a continuous engineering sensor capable of reading how the infrastructure behaves thermally.
This marks the shift from reactive alarms to predictive insight—from responding after an event to understanding risk before it emerges.
