Leak detection systems have long been used to protect critical infrastructure such as data centres, plant rooms, and commercial buildings. Technologies such as leak detection cables are widely deployed to identify water ingress that could cause operational disruption or costly damage.
However, traditional monitoring systems often rely on fixed alert thresholds that do not always reflect real-world environmental conditions. According to Quensus, improving how these signals are interpreted could significantly enhance the reliability of leak detection and support more proactive risk management.
How leak detection cables operate
Leak detection cables typically generate a numerical reading that reflects moisture exposure. These readings are commonly measured on a scale between 0 and 5000.
Under normal dry conditions, cables installed in well-controlled environments usually produce low baseline readings. In many cases this baseline falls between 3 and 10.
When water is present, readings increase sharply. Even relatively small leaks can raise values above 100, while larger incidents can generate significantly higher readings.
Most monitoring systems rely on a fixed threshold to trigger alerts. Once readings exceed that predefined level, an alarm is activated.
While this approach appears straightforward, real-world conditions often introduce additional complexity.
Environmental factors can distort readings
Leak detection systems installed in operational environments are exposed to a range of variables that can influence signal readings. Temperature changes throughout the day, humidity fluctuations, condensation cycles, and proximity to conductive building materials can all affect measurements.
Other factors such as electrical interference, installation conditions, and ageing infrastructure may also contribute to variability in the readings generated by leak cables.
When systems rely solely on fixed thresholds, these environmental influences can trigger frequent false alerts. Over time, repeated false alarms can reduce confidence in monitoring systems and create unnecessary operational noise for facilities teams.
In some cases, persistent false alerts may even lead teams to ignore notifications entirely, undermining the purpose of the monitoring system.
Introducing adaptive baseline analysis
To address these challenges, Quensus engineers have been developing an alternative method of interpreting leak detection signals known as filtered average baseline analysis.
Rather than relying on static thresholds, this approach continuously analyses historical sensor readings to establish a dynamic baseline that reflects the normal environmental behaviour of a particular installation.
Alert sensitivity can then be adjusted relative to this evolving baseline. This allows the system to filter out background signal noise while still detecting unusual patterns that may indicate genuine water leaks.
Initial testing suggests the approach can significantly reduce false alerts while maintaining sensitivity to real incidents.
Supporting preventative water risk management
Reducing false alarms is not simply a matter of operational convenience. Reliable monitoring signals can help facilities teams respond more quickly to genuine problems and improve confidence in automated response systems.
More accurate data also supports preventative water management strategies. When monitoring platforms can reliably distinguish between environmental noise and real leak events, automated systems such as shut-off valves can be triggered with greater confidence.
This shift allows organisations to move beyond reactive investigation and toward preventative protection against water damage.
The growing role of data interpretation
As building infrastructure becomes increasingly connected, the focus of monitoring technology is shifting from simply collecting data to interpreting it more intelligently.
Advanced analytics, adaptive thresholds, and behavioural modelling are becoming essential tools for identifying risks earlier and reducing operational disruption.
According to Quensus, improvements in data interpretation will continue to play an important role in strengthening infrastructure resilience, particularly in environments where water damage represents a significant operational and financial risk.
Read the full blog from Quensus here.
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