Visualize and interpret fluid‑related telematics signals to anticipate wear, leaks and contamination long before they cause breakdowns. A signal map transforms raw data into predictive insights for fleet managers.
Map and monitor telematics data streams across your fleet’s critical fluids to uncover patterns you can act on.
Signal mapping is the backbone of predictive maintenance for fluids. Combine data from oil analysis labs, telematics devices and machine control systems to build a holistic picture of your fleet’s health. Learn how signal maps complement other analytics like vibration thresholds and battery life models for a comprehensive predictive strategy.
| Signal | What It Reveals | Actionable Threshold |
|---|---|---|
| Oil Temperature | Indicates lubricant condition and cooling system health | >220°F triggers inspection |
| Viscosity Index | Measures lubricant thickness relative to temperature | Drop of 15% requires oil analysis |
| Contaminant Count | Tracks dirt, soot or metal particles in fluids | >100 ppm signals wear or leak |
| Fluid Pressure | Shows pump performance and system resistance | Variations >15% indicate blockages |
Building an effective signal map requires accurate data integration, correct sensor placement and advanced analysis.
Follow these steps to create and refine a telematics signal map that drives predictive fluid maintenance.
Identify which fluid parameters to track based on component criticality and failure modes.
Deploy sensors and integrate telematics hardware. Calibrate devices to ensure data accuracy.
Aggregate signals in dashboards, identify patterns and correlate with maintenance events.
Use insights to adjust service intervals and prevent failures. Continually refine thresholds.
Telematics signal maps unlock actionable insights that extend component life and reduce maintenance costs.
Earlier detection of fluid issues
Reduction in unscheduled downtime
Extension of fluid life
Maintenance cost savings
"After implementing a telematics signal map, we uncovered high contaminant counts in our hydraulic lines. Acting quickly, we prevented several pump failures and extended fluid life by 15%. Our service team now relies on signal dashboards to schedule maintenance proactively."
Fleet Manager
Find answers to common questions about building and using telematics signal maps for fluid analysis.
A telematics signal map is a visual representation of multiple sensor data streams—such as temperature, pressure, viscosity and contamination—mapped over time and correlated with equipment performance. It allows maintenance teams to identify anomalies and predict failures.
Select sensors based on the types of fluids you monitor and the criticality of components. Ensure sensors are compatible with harsh environments and that they integrate easily with telematics hardware.
Yes. Advanced maintenance platforms combine signal maps with AI algorithms to forecast failures. Data scientists can train models on historical sensor data to predict when critical fluid thresholds will be exceeded.
Establish baselines by collecting signal data during normal operations. Consult manufacturers’ guidelines and industry standards. Adjust thresholds as you gather more data and understand what constitutes abnormal behaviour for your fleet.
Signal maps are often combined with vibration analysis, failure probability modelling and AI‑driven anomaly detection to provide a complete predictive maintenance solution.
Explore these related resources to enhance your predictive maintenance program.
Monitor vibration patterns to uncover early signs of component fatigue.
View ThresholdsSet up automatic alerts based on oil quality and contaminant levels.
View AlarmsDefine triggers that schedule service based on fluid condition instead of time.
View TriggersEnhance your predictive strategy with these related categories.
A telematics signal map helps you make proactive, data‑driven decisions about fluid health and maintenance scheduling. Don’t wait for problems—anticipate them.
Visualise signals across your fleet in one intuitive dashboard.
Use AI to forecast when fluids will fall outside acceptable ranges.
Automate alerts and service requests to prevent downtime.